Eco-efficiency analysis of Spanish WWTPs using the LCA + DEA method

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Eco-efficiency analysis of Spanish WWTPs using the LCA þ DEA method
zquez-Rowe a,b, Sergio Chenel c, Yago Lorenzo-Toja a,*, Ian Va Desir
ee Marı´n-Navarro c, Marı´a Teresa Moreira a, Gumersindo Feijoo a a

Department of Chemical Engineering, Institute of Technology, University of Santiago de Compostela, 15782 Santiago de Compostela, Galicia, Spain b Peruvian LCA Network, Department of Engineering, Pontificia Universidad Cat
olica del Peru´, 1801 Avenida Universitaria, San Miguel, Lima 32, Peru c  de Llobregat, Barcelona, Spain CETaqua, Water Technology Centre, 08940 Cornella

article info

abstract

Article history:

Wastewater treatment plants (WWTPs) are regarded as units designed for the efficient

Received 29 May 2014

removal of organic matter and nutrients from polluted wastewaters, avoiding their

Received in revised form

discharge into the environment. Despite these benefits, they have also been found to be

8 October 2014

highly energy intensive, with consequent increased emissions in terms of greenhouse

Accepted 12 October 2014

gases and other environmental impacts. Therefore, it has become imperative to monitor

Available online 29 October 2014

thoroughly the overall functioning of WWTPs from an integrated perspective with the aim of understanding how these can improve their eco-efficiency. In this case study, a group of

Keywords:

113 WWTPs located in regions across Spain were analysed using the methodology that

Data Envelopment Analysis

combines life cycle assessment (LCA) and data envelopment analysis (DEA). The aim of this

Eutrophication

work was to determine the operational efficiency of each unit in order to obtain environ-

Life Cycle Assessment

mental benchmarks for inefficient plants. Thereafter, the environmental gains linked with

Wastewater

the inputs reduction proposed for the DEA model for each unit were computed in order to verify eco-efficiency criteria. The operational complexity of WWTPs resulted in several identified factors affecting their efficiency which are discussed in depth, including the size of the facility, the climatic influence, the influent load and the over- or underuse of the plant. © 2014 Elsevier Ltd. All rights reserved.

1.

Introduction

Conventional wastewater treatment processes have been applied since the early 20th century, when Ardent and Lockett first described the activated sludge process in the United Kingdom. For the past 100 years, wastewater treatment plants

(WWTPs) have been widespread all over the world in order to prevent human populations from disease, as well as avoiding ecosystems from degrading (Metcalf and Eddy, 2003). In fact, WWTPs have been increasingly designed to be very flexible within their operation (Hopkins et al., 2001). For instance, it is common for WWTPs to suffer important changes in the flow

* Corresponding author. E-mail addresses: [email protected], [email protected] (Y. Lorenzo-Toja). http://dx.doi.org/10.1016/j.watres.2014.10.040 0043-1354/© 2014 Elsevier Ltd. All rights reserved.

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rate and wastewater composition entering the plant. Thus, the analysis of their efficiency can appear as a challenging task. Actually, previous studies dealing with efficiency analysis of WWTPs have been focused on economic and productivity aspects of the facilities (Sala-Garrido et al., 2012). For
ndez-Sancho and Sala-Garrido (2009) worked instance, Herna on the assessment of the economic efficiency of several WWTPs along the Spanish Mediterranean coast. Their findings highlighted the usefulness of this type of analysis, since they offer a detailed overall picture of possible reductions in the use of operational inputs that are congruous with a certain output (effluent with fixed quality standards). Additional studies also analysed the changes in the productivity of WWTPs in urban environments (Marques and Monteiro, 2003; Byrnes et al., 2010), or the impact of privatisation and regulation processes in the water industry (Saal and Parker, 2000, 2001). A wide range of factors influence the performance of WWTPs. In fact, one of the most important driving forces affecting the efficiency of WWTPs has been shown to be the characteristics of the influent. Highly loaded influents usually imply satisfactory levels of pollutant removal. In contrast, diluted influents are prone to causing operational issues. Some additional factors affecting efficiency may not be so evident, such as seasonal variation due to tourism, which implies that WWTPs in tourist areas operate at full capacity during certain holiday periods throughout the year, but suffer ~ oz and underuse problems during the rest of the year (Mun Caus, 2005). Despite the obvious environmental benefits linked to the main purposes of WWTPs (Macleod and Haygarth, 2010; Gracia-Lor et al., 2012), including the removal of coarse solids and organic pollutants (e.g., dissolved organic matter, solids and nutrients), there are some important drawbacks linked to their operation in terms of sustainability (Molinos-Senante et al., 2011). These include aspects that affect environmental protection, as well as social and economic development (Balkema et al., 2002). In this sense, some studies have identified that certain operational inputs in WWTPs, such as the use of energy or chemicals, as well as the treatment of the wastes produced result in a rise of the total environmental impact linked to the treatment process (Hospido et al., 2004). An internationally standardised methodology to provide an integrated assessment of the environmental profile of products, named life cycle assessment (LCA), has been applied in wastewater treatment to identify the main environmental impacts from a life-cycle perspective (Emmerson et al., 1995; Hospido et al., 2004; Pasqualino et al., 2009; Rodrı´guez-Garcia et al., 2011a). Through time these studies have become increasingly specialised, dealing with the assessment of conventional active sludge technologies and non-conventional ones (Høibye et al., 2008), different sludge management strategies (Hospido et al., 2010) or the influence of system boundaries and scale (Lundin et al., 2000). Beyond the improvement actions that can be undertaken in individual WWTPs, such as the implementation of clean technologies or best available techniques (BATs), these strategies should be oriented towards a larger scope in which the use of resources and the subsequent reduction of environmental impacts should meet levels that are in accordance

with the Earth's carrying capacity (Schmidheiny, 1992; Schmidheiny and Stigson, 2000). Therefore, in the current study the analysis of the eco-efficiency of a large sample of WWTPs located throughout the Spanish geography is proposed. In order to fulfil this goal this research paper proposes the joint use of LCA with data envelopment analysis (DEA), a nonparametric method used for the estimation of production frontiers in operations research and economics (Cooper et al.,
zquez-Rowe et al., 2010; Va
zquez-Rowe and Iribarren, 2007; Va 2014). This combined method allows the estimation of specific operational benchmarks to monitor the performance (i.e., ecoefficiency) of a wide range of comparable units (Iribarren et al., 2014). Once these are calculated through a DEA matrix that nourishes from life cycle inventory (LCI) data, the potential environmental gains for inefficient units (in this case WWTPs) are computed using LCA. The LCA þ DEA method has been applied to several production sectors in recent years, such as
zquez-Rowe et al., 2011; Avadı´ et al., 2014), dairy fisheries (Va farms (Iribarren et al., 2011), energy (Iribarren et al., 2013) or
zquez-Rowe et al., 2012; Mohammadi agri-food products (Va et al., 2013), proving to be a useful method in the field of eco-efficiency and industrial ecology in scenarios in which
zquez-Rowe multiple inventory datasets are available (Va
zquez-Rowe and Iribarren, 2014). et al., 2010; Va The present LCA þ DEA analysis is part of the European funded AQUAENVEC project, which delves into the energy and material flows in WWTPs in a large sample of Spanish WWTPs in order to improve the economic and environmental efficiency of these units (AQUAENVEC, 2012). Through the computation of the results, the main aim of the case study is to support decision-making when designing and managing WWTPs, by: i) providing a set of best practice target operational values, ii) estimating the environmental impact improvements linked to the target operational reductions, and iii) the identification of specific improvement actions at an operational level to attain, at least partially, the theoretical target operation points.

2.

Materials and methods

2.1.

Definition of the case study

2.1.1.

Contextualisation of the study

WWTPs are complex systems with multiple operational inputs that are in constant change (Fig. 1). For instance, the load of the influent entering the plant presents high levels of variability, that is usually influenced by a wide range of local factors, such as precipitation or other climatic parameters, seasonal trends or the characteristics and proportion of the wastewater types that merge at the plant (Rodrı´guez-Garcia et al., 2011b). One of the main methodological issues is the definition of the function of the system as it serves as the basis to establish two key elements of a life-cycle assessment: the functional unit (FU) and the system boundaries. In fact, the consideration of the function of a WWTP as just the removal of pollutants from the influents leads to some limitations in the interpretation of the results obtained, since the quality of the effluent and the treatment efficiency are neglected (Silva et al., 2014). For

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Fig. 1 e Schematic representation of the system boundaries of the wastewater treatment (WWTP) system under assessment. Note that the figure is adapted to all the different WWTPs sizes.

instance, despite the removal of organic matter, solids and nutrients being the key objectives in the operation of a WWTP, the eutrophication potential linked to the discharge of the treated effluent corresponds to the main environmental impact in most plants (Rodrı´guez-Garcia et al., 2011a). Hence, based on a more accurate understanding of the efficiency of removal, Godin et al. (2012) developed a methodology that allows comparing a WWTP scenario and a null option scenario. This more WWT-oriented approach, named net environmental benefit (NEB) or Net Impact as in Igos et al. (2012), studies the difference between the direct discharge of wastewater to a water body considered as the null option and a wastewater alternative, as explained in more detail in Section 2.2.4 and in the Supplementary Information e SI (Godin et al., 2012). Furthermore, an important point when defining the function of the WWTP is the size of the facility in terms of design population equivalents (p.e), since it will influence the type of treatment and technology used (Gallego et al., 2008). In fact, a division in ranges WWTPs based on their p.e allows a better definition of the function of the system and comparing facilities with a common objective, as explained in further detail in Section 2.2.3 (Tillman et al., 1998; Lundin et al., 2000).

2.1.2.

Definition of the unit of assessment

A unit of assessment that will be used for computation in the DEA model has to be selected, which is named decision making unit e DMU (Cooper et al., 2007). Given the aims of the study, and in accordance with previous studies that have examined WWTPs using DEA methodology (Sala-Garrido et al., 2012), the unit of assessment (i.e., DMU) selected is the WWTP, since it constitutes the most reliable and homogeneous unit to which the results can be referred to (see Fig. 1). Each DMU will be then supported by a comprehensive inventory of the main inputs and outputs flows, which will be thereafter used in the DEA matrix to non-parametrically estimate their individual efficiency based on the DEA optimisation model described in Section 2.2.2. The model inserted in the matrix will then estimate a production efficient frontier, which is the aggregation of the best performing DMUs (Cooper et al., 2007). These are considered to be part of the reference set and are, therefore, efficient (F ¼ 1), while the remaining

units are located in the production possibility set (PPS) and present different levels of inefficiency with respect to the reference set. The final efficiency score, which aggregates the individual input/output ratios per DMU (Avadı´ et al., 2014), is accompanied by the computation of target operating values for improved efficiency (Cooper et al., 2007). A total of 470 WWTPs across the Spanish geography were inventoried in this case study to develop the LCA þ DEA assessment. However, the final number of DMUs actually assessed was reduced to 113 due to a series of data gaps and quality, on the one hand, and methodological issues, on the other, all of which are described and justified throughout the analysis.

2.2.

The LCþDEA framework

2.2.1.

The five-step LCA þ DEA method

LC (life-cycle) þDEA methods can be divided in two main
zquez-Rowe groups: energy and environmental approaches (Va and Iribarren, 2014). In this particular study, an environmental LCþDEA method was selected for the computation of the results based on the objectives of the study: to understand the eco-efficiency of a wide range of WWTPs across Spain and to propose a series of benchmarks to improve their environmental profile. For this, a five-step LCA þ DEA method, as
zquez-Rowe et al. (2010), was implemented that presented in Va allows the minimisation of operational inputs to guarantee a reduction in operational inefficiencies and, subsequently, environmental impacts throughout the sample assessed. The five-step LCA þ DEA method, as can be seen in the form of a schematic representation in the graphical abstract, is structured as follows: i) collection of data individually for each unit of assessment to build the LCI and organise the DEA matrices; ii) calculation of the current environmental profile of the DMUs through the computation of the life cycle impact assessment (LCIA); iii) implementation of the DEA model to obtain the efficiency scores and target reference values for each DMU; iv) estimation through a second LCIA stage of the target environmental impacts linked to inefficient entities based on the benchmarks obtained in step 3; and v) ecoefficiency verification through the interpretation of the

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results obtained, as well as analysis of feasible improvement actions to attain the target performance benchmarks
zquez-Rowe et al., 2010). However, it (Iribarren et al., 2010; Va should be noted that in a similar way as in Avadı´ et al. (2014), it was necessary to provide a modification of the method in order to compute certain inputs in a homogeneous form. More specifically, the chemicals used in the different WWTPs are highly variable, with different characteristics and amounts being used. Nonetheless, the use of chemicals and their associated environmental impacts due to their transportation by road to the facility have shown to have a relevant impact on the overall environmental profile of WWTPs (Hospido et al., 2008). Therefore, with the aim of assuring a homogeneous quantification of these chemicals in the DEA matrix, the weighted ReCiPe endpoint LCIA method was used to attain a final single indicator value for the cluster of chemicals that are being used in the WWTPs assessed (Goedkoop et al., 2009). In other words, chemicals that are included in the chemical consumption input in the DEA matrix, as shown in Section 3, will suffer direct environmental benchmarking in step IIb (see graphical abstract), instead of a two-step environmental benchmarking observed for regular operational inputs (Avadı´
zquez-Rowe and Iribarren, 2014). et al., 2014; Va

2.2.2.

DEA model selection

A range of different models were tested for the DEA matrices that were built for this case study. The assessed models included variants of the Charnes-Cooper-Rhodes (CCR), the Banker-Charnes-Cooper (BCC) and the assurance region (AR) models (Cooper et al., 2007). However, the model that was finally selected to compute the results was the slacks-based measure of efficiency (SBM).1 This selection was done based on its elasticity regarding the calculation of the inefficiencies for the different entities (i.e., DMUs), since it performs the computation regardless the units of measure used for the different inputs and outputs (Cooper et al., 2007; Thrall, 1996). In a similar way, unlike CCR and BCC models, the SBM model considers non-radial characteristics of inputs and outputs, which makes it more appropriate for monitoring inputs with vague interconnections (Cooper et al., 2007). In addition, according to Tone (2001), the SBM model accounts for all inefficiencies, whereas other models (i.e., CCR) only take into consideration purely technical efficiency. Finally, the SBM model provides a series of target values for the minimised inputs and/or outputs that deliver appropriate benchmarks to calculate the target theoretical environmental profile of inefficient DMUs (Cooper et al., 2007). The SBM model allows assigning weights to the selection of inputs and outputs included in the DEA matrix (Cooper et al., 2007). While this feature may be useful in some LCþDEA approaches, such as CFP (carbon footprinting) þDEA, in which the main sources of environmental impact are easier to detect due to the single environmental dimension that is taken into consideration. However, in LCA þ DEA methods, given the inclusion of a wide range of environmental impact categories, weighting is not recommended due to the variable role of operational inputs in the final impacts per category. 1

Section S6 in the Supplementary Information (SI) provides the mathematical formulation of the SBM model.

An input-oriented approach was selected for this case study, in accordance with previous LCA þ DEA studies (Avadı´ et al., 2014). This perspective was chosen based on the main objective of minimising the use of resources and, therefore, an optimisation of operational inputs, while maintaining the
zquez-Rowe et al., 2010; Va
zquezquality of the effluent (Va Rowe and Iribarren, 2014). Finally, constant returns-to-scale (CRS) were assumed in the final selection of DEA matrices, since these were aggregated based on their operational size, limiting the effects of scale factors between WWTPs (Banker et al., 1984; Cooper et al., 2007).

2.2.3.

Selection of DEA matrices

A total of three different DEA matrices were selected to perform the assessment. This structure was based on the assumption that it was necessary to disaggregate the 113 WWTPs in order to perform an accurate assessment. A parallel study within the AQUAENVEC (2012) project that analysed approximately 80 WWTPs from Atlantic and Mediterranean regions in Spain determined that these plants could be aggregated into three main blocks linked to the p.e. they were designed to treat, since the technology used and the function of the WWTP in each of these groups is highly comparable, whereas important technological leaps were observed between the groups. Consequently, three different ranges were fixed, each of which represent three different DEA matrices with a variable number of DMUs, depending on the available data: i) WWTPs between 0 and 20,000 p.e. (Small WWTPs); ii) WWTPs between 20,001 and 50,000 p.e. (Medium WWTPs); and iii) WWTPs treating wastewater above 50,000 p.e. (Large WWTPs). Nevertheless, two important methodological assumptions should be noted. On the one hand, the p.e. calculated for the different WWTPs were based on actual operating capacity (considering 2011 operational data), rather than on the original design of each plant, since this allows to have a more realistic perspective of the plant. In addition, considering the real load of the plant allowed identifying facilities with over- or under-use concerns, and issue discussed in detail in section 4.2.2. On the other hand, despite the variable number of DMUs in each matrix, they all met the rule of thumb regarding the number of inputs and outputs required without affecting any statistical constraint (Cooper et al., 2007).2

2.2.4.

Input and output selection for the DEA matrices

Three different inputs were included in the DEA matrices: i) electricity use at the WWTPs; ii) chemical consumption; and iii) sludge production. Inputs 1 and 3 involved the direct computation of the inventory data for these items, which can be considered standard practice in most LCþDEA studies
zquez-Rowe et al., 2010, 2011, 2012; Iribarren et al., 2011). (Va However, input 2 (i.e., production of chemicals), as described previously in Section 2.2.1 and in Avadı´ et al. (2014), did not refer to an individual inventory item, but to a series of

2

The rule of thumb which is used to determine the minimum sample size in a particular DEA matrix is: n  max {m  s, 3  (m þ s)} (Cooper et al., 2007), where m represents the number of inputs used in the DEA matrix and s is the number of outputs.

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chemicals with heterogeneous characteristics but one common function. The selected output used in the DEA matrices must summarise the function of the WWTP. As previously mentioned in Section 2.1, the definition of the function is a challenging and important task when analysing these units. Several commonly used FUs in LCA studies were tested as possible outputs including kg of suspended solids removed or cubic metre of treated water or kg of PO3 4 removed. Finally, the NEB indicator was chosen since it provides the most accurate approach to the operational objective of a WWTP: eutrophication potential reduction. The NEB therefore results from the difference between the avoided and induced potential environmental impacts of the WWTP as represented in the SI. The selection of inputs and outputs, as shown in Fig. 2, allowed capturing to a great extent the depth of the LCI in the DEA matrix, encompassing all those inputs that have repeatedly shown to account for an overwhelming portion of the environmental impacts identified in previous LCA studies on WWTPs (Hospido et al., 2004; Rodrı´guez-Garcı´a et al., 2011a,b).

3.

Results

3.1.

Inventory data

The collection of inventory data for the computation of lifecycle environmental management tools should be intensive and thorough (ISO, 2006a,b). Moreover, DEA allows the

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individual assessment of homogeneous sets of entities (in this case WWTPs). This implies that the application of the LCA þ DEA method requires a meticulous and exhaustive data collection phase for each of the selected DMUs in order to guarantee the consideration of their particular characteristics and, subsequently, the feasibility and robustness of the final results (Iribarren et al., 2011). As previously mentioned, a total of 470 WWTPs were inventoried for their assessment. Data were provided mainly by the Water Technology Centre (CETAQUA) in the form of vast Excel sheets in which a range of operational inputs and outputs were included (see Table 1 for those relevant for the computation of an LCA þ DEA assessment). Regarding operational values like volume of treated water, chemical consumption or energy use among others, the record books of each plant were used as the primary source. Data were provided on an annual basis for years comprised between 2009 and 2012. However, the depth of data availability was substantially higher for year 2011, which led to the examination of this particular year. Despite the numerous data available, a wide range of plants were discarded for their final use in the LCA þ DEA assessment due to a series of determining factors. Firstly, those WWTPs that presented important data gaps, based on the selection of inputs and outputs discussed in Section 2.2.4, were discarded leaving the remaining WWTPs at 162. In a second phase, a set of meetings were held between the LCþDEA practitioners and WWTPs technicians to evaluate the quality of the data in the remaining WWTPs. In this stage, a

Fig. 2 e Life cycle inventory (LCI) and DEA items included for each DMU.

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Table 1 e Main average inventory data of the case study per WWTP size (with standard deviations in brackets; values per m3 of treated water). WWTP size based on the range population equivalent 0e20,000 Influent COD (g) TSS (g) TN (g) TP (g) Effluent COD (g) TSS (g) TN (g) TP (g) Wastes Sludge (kg) U.S.W (g) Grit (g) Grease (g) Electricity From the grid (kWh) Chemicals Polyelectrolite (g) FeCl3 (g) NaClO (g) Others (g)

20,000e50,000

>50,000

516 (±269) 238 (±125) 52.4 (±24.7) 7.02 (±4.25)

662 308 48.5 6.75

(±206) (±102) (±25.0) (±8.97)

666 308 53.8 8.55

(±291) (±117) (±24.9) (±6.02)

49.3 (±48.1) 15.1 (±15.0) 13.7 (±8.97) 2.41 (±1.37)

78.8 23.0 16.7 1.75

(±105) (±19.8) (±13.3) (±1.80)

56.50 16.2 26.6 2.05

(±36.3) (±17.1) (±16.2) (±1.29)

1.24 (±1.48) 29.1 (±61.5) 17.3 (±44.7) 5.70 (±27.6)

1.04 22.8 31.2 3.25

(±0.41) (±15.4) (±44.8) (±4.69)

0.93 27.9 26.7 3.76

(±0.52) (±23.5) (±29.8) (±9.36)

0.89 (±0.92)

0.45 (±0.13)

0.41 (±0.27)

1.98 (±1.94) 3.18 (±12.6) 18.9 (±148) 3.50 (±27.9)

2.10 2.65 0.03 3.60

2.50 19.5 1.24 10.8

(±1.31) (±5.97) (±0.12) (±9.63)

(±2.53) (±29.1) (±3.52) (±30.6)

COD ¼ chemical oxygen demand; TSS ¼ total suspended solids; TN ¼ total nitrogen; TP ¼ total phosphorus; U.S.W ¼ urban solid waste.

set of 19 WWTPs were discarded given the lack of reliable data, since they did not meet the established quality thresholds: daily samples for wastewater parameters, lack of important data regarding operational inputs, etc. More specifically, the shortage of data quality was mainly identified in Small WWTPs that lack continuous operational control. Finally, in a third stage the WWTPs were divided in two main blocks depending on whether their treatment chain presented an operating tertiary treatment system or not. The rationale behind this discrimination was based on the fact that the main objective of WWTPs is to reduce the amount of organic matter, suspended solids and nutrients that contributes to eutrophication potential in wastewater. While this target is common to all WWTP, those that operate some type of tertiary system (e.g., chlorination, active carbon, ozonisation, etc.) not only perform this goal, but also improve the quality of the water by reducing the content of other types of pollutants, such as pathogens, pharmaceuticals and personal ~ oz et al., 2009). Therefore, this freecare products (Mun microbial effluent can be used safely for water reuse purposes. Consequently, WWTPs with or without tertiary systems were not considered to conform similar entities. Thereafter, it was proposed to eliminate those operational inputs that affect WWTPs with tertiary treatment only, in order to make them directly comparable to WWTPs without these final treatments. However, the level of detail of the data provided by CETAQUA did not allow to follow this strategy. In

other words, plants with tertiary treatment would be penalised for increased energy and chemical use in the minimisation stage of the LCA þ DEA method, when these units are actually providing an improved final product (Ortiz et al., 2007). Unfortunately, the number of WWTPs available with operating tertiary systems were not sufficient to conduct independent DEA assessment for this group. Hence, the scope of the study was limited to those WWTPs with good data quality that lacked an operating tertiary treatment system in operation. The final sample size used in the case study comprised a total of 113 WWTPs spread out through most of Spain, as can be observed in Fig. 3. This sample represented 6.85% of the total amount of population equivalent treated in Spain in 2011 (Ministry of the Environment, 2014), which demonstrates the significance of these data. The electricity mix used for the study was modelled based on the ecoinvent® database, but updating the data for the average electricity production and import/export data for
ctrica Espan ~ ola, 2011). The medium Spain in 2011 (Red Ele voltage electricity used in WWTPs was subsequently modelled by adding the transformation from high voltage, direct SF6 emissions to air and electricity losses due to transportation systems. Finally, data for background processes were taken from the ecoinvent® database (Frischknecht et al., 2007).

3.2. Life Cycle Impact Assessment (LCIA) of current DMUs The Life Cycle Inventory built for each individual WWTP (i.e., DMU) was implemented in SimaPro 8.0 with the aim of performing the Life Cycle Impact Assessment e LCIA (Goedkoop et al., 2010). The results were computed using the ReCiPe midpoint and endpoint hierarchist assessment methods. The main difference between the two methods is linked to the environmental perspective they assume. Midpoints are defined as a parameter in a cause-effect chain or network for a particular impact category that is between the inventory data and the category endpoints, whereas endpoints reflect differences between stressors at an endpoint in a cause-effect chain and may be of direct relevance to society's understanding of the final effect, such as measures of biodiversity changes (Bare et al., 2000). For the midpoint methodology, results were calculated for all the individual impact categories included in this method (see Table S1 in the Supplementary Information to see the entire list), whereas for the endpoint the results were reported using the single score weighted value3 with the aim of providing one single value of reference for each DMU. The FU, which is the unit selected to which the LCIA results are referred to (ISO, 2006a), was fixed as 1 m3 of wastewater treated by the WWTP in the year 2011. Fig. 4 illustrates the average single score endpoint value per each of the three WWTP segments (see Table S1 in the SI for the midpoint results per WWTP group). 3

The specific perspective that was selected in this study was the hierachist one, which considers a weighting of 40% for human health impact categories, 40% for ecosystem quality categories and 30% for resources categories.

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Fig. 3 e Location of the wastewater treatment plants assessed in Spain by size: <20,000 population equivalent (small dark blue dots), 20,000e50,000 population equivalent (medium blue dots) and >50,000 population equivalent (large clear blue dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.3.

DEA computation and efficiency scores

The DEA matrices were elaborated based on the available LCI and according to the discussion performed in section 2.2.4 concerning the inclusion and exclusion of inputs and/or outputs in the matrix. As mentioned in section 2.2.3, a total of three different matrices were structured based on the size of

the WWTPs (see Tables S2, S3 and S4 in the Supplementary Information - SI). These three matrices were introduced in the DEA-Solver Professional Release 10.0 software (Saitech, 2014) and ran under an input-oriented SBM (SBM-I) model as previously mentioned. The solution of a DEA optimization model leads to an efficiency score and to the definition of operational targets

Fig. 4 e Average single score (ReCiPe endpoint H) environmental impacts for each segment size.

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Table 2 e Target reduction percentages for operational inputs and efficiency scores (%) for small size (<20,000 population equivalent) wastewater treatment plants e WWTP. DMU

1 2 3 4 5 7 8 9 11 12 13 14 16 17 18 19 20 22 23 24 25 27 28 29 30 32 33 34 36 38 39 40 41 43 44 45 46 47 50 51 53 54 55 56 59 61 64 65 67 68 72 73 75 77 80 83 84 85

Inputs Electricity (%)

Chemical consumption (%)

Sludge production (%)

88.11 69.02 69.41 63.45 46.55 65.05 48.45 65.24 57.01 77.79 84.91 86.23 44.63 41.67 54.93 27.56 71.48 83.33 47.87 72.45 65.23 65.68 69.56 62.57 80.85 52.18 93.20 49.51 60.51 0.00 70.23 78.28 89.77 79.83 0.00 48.58 82.27 61.19 91.00 69.35 94.95 0.00 49.58 25.85 65.80 49.60 24.07 0.00 65.67 72.03 73.39 95.32 9.71 59.04 32.27 42.06 44.09 20.24

94.77 70.23 90.61 65.00 81.36 76.46 41.49 81.53 75.70 76.11 95.78 97.17 53.25 49.82 78.32 70.67 85.33 96.99 77.97 82.59 77.65 81.52 93.17 81.32 93.51 65.27 93.64 78.15 72.57 0.00 98.07 98.53 96.70 76.21 0.00 78.99 88.24 64.40 95.16 77.21 94.80 0.00 88.55 88.15 77.13 68.08 71.64 0.00 72.18 97.96 54.74 99.69 36.81 94.30 69.04 55.00 76.66 87.85

91.79 76.23 88.56 86.77 81.60 87.56 58.68 52.01 74.46 99.08 95.12 91.87 53.21 82.10 69.77 71.54 91.37 96.75 81.39 83.80 84.14 87.97 41.64 84.26 89.84 78.82 93.61 74.26 77.77 0.00 52.18 89.20 72.06 87.58 0.00 75.79 77.93 76.29 93.92 79.31 91.01 0.00 74.18 76.70 88.15 71.17 44.51 0.00 77.17 86.46 71.74 93.62 26.96 68.57 63.57 50.73 77.03 69.22

Table 2 e (continued ) DMU

Efficiency (%)

8.44 28.18 17.14 28.26 30.16 23.64 50.46 33.74 30.95 15.68 8.06 8.24 49.64 42.14 32.33 43.41 17.28 7.64 30.92 20.39 24.33 21.61 31.88 23.95 11.93 34.58 6.52 32.69 29.72 100.00 26.51 11.33 13.82 18.79 100.00 32.21 17.19 32.71 6.64 24.71 6.42 100.00 29.23 36.43 22.97 37.05 53.26 100.00 28.33 14.52 33.38 3.79 75.51 26.03 45.04 50.74 34.07 40.90

86 87 89 90 91 92 93 96 98 99 101 102 105 106 108 109 110 112 113

Inputs Electricity (%)

Chemical consumption (%)

Sludge production (%)

47.09 93.83 64.23 68.43 84.49 39.39 58.16 61.73 36.89 73.47 45.00 54.07 44.41 83.63 69.86 79.24 0.00 93.01 78.42

90.92 91.36 69.44 88.46 96.87 76.09 93.99 84.31 44.03 82.41 77.64 95.33 70.94 84.44 79.10 94.41 0.00 78.29 92.39

80.35 90.58 38.36 72.56 93.10 79.83 84.97 84.84 56.71 46.74 47.74 81.06 66.82 73.19 72.79 84.31 0.00 89.97 91.12

Efficiency (%)

27.22 8.07 42.66 23.52 8.51 34.90 20.96 23.04 54.13 32.46 43.21 23.18 39.28 19.58 26.08 14.01 100.00 12.91 12.69

DMU ¼ decision making unit.

for the selected inputs and output; these results are presented in Tables 2e4. Only a total of 11 facilities were found fully efficient (i.e., efficiency score of 100%). Regarding the rest of WWTPs under assessment, 60% of the total ranges between 25% and 75% of efficiency. This allowed important input target reductions that are expected to generate notable minimisation opportunities in the environmental impacts. Non-efficient WWTPs were defined within each range of p.e.. The target reduction percentages proposed in Tables 2e4 for the operational values would potentially allow the

Table 3 e Target reduction percentages for operational inputs and efficiency scores (%) for medium size (20,000e50,000 population equivalent) wastewater treatment plants e WWTP. DMU

6 10 15 31 35 49 58 60 62 63 78 81 88 111

Inputs Electricity (%)

Chemical consumption (%)

Sludge production (%)

48.09 73.01 0.00 48.82 72.97 0.00 63.41 98.64 96.86 22.73 0.00 36.47 51.17 91.47

78.22 64.83 0.00 60.78 77.46 0.00 62.05 96.14 94.70 56.90 0.00 71.69 95.05 75.90

27.56 59.73 0.00 0.00 55.24 0.00 43.22 98.45 94.43 69.88 0.00 28.93 25.97 88.44

DMU ¼ decision making unit.

Efficiency (%)

48.71 34.14 100.00 63.47 31.44 100.00 43.77 2.26 4.67 50.17 100.00 54.30 42.60 14.73

w a t e r r e s e a r c h 6 8 ( 2 0 1 5 ) 6 5 1 e6 6 6

Table 4 e Target reduction percentages for operational inputs and efficiency scores (%) for large size (>50,000 population equivalent) wastewater treatment plants e WWTP. DMU

21 26 37 42 48 52 57 66 69 70 71 74 76 79 82 94 95 97 100 103 104 107

Inputs Electricity (%)

Chemical consumption (%)

Sludge production (%)

0.25 0.00 43.78 80.96 14.08 39.07 63.43 0.00 16.19 67.14 8.89 48.03 59.14 61.05 0.00 0.00 47.51 0.00 24.06 49.20 56.15 23.26

82.37 67.36 37.79 58.30 49.04 20.97 86.19 0.00 72.28 96.67 72.74 86.62 91.04 96.94 0.00 94.29 81.11 0.00 35.12 93.02 57.86 91.78

67.43 44.94 50.14 83.24 52.63 42.29 3.49 0.00 39.74 82.58 23.58 57.11 80.60 77.44 0.00 30.41 77.37 0.00 65.27 45.67 70.28 62.60

Efficiency (%)

49.98 62.57 56.10 25.83 61.42 65.89 48.96 100.00 57.26 17.87 64.93 36.08 23.07 21.53 100.00 58.43 31.34 100.00 58.52 37.37 38.57 40.79

DMU ¼ decision making unit.

inefficient WWTPs to operate under fully efficient conditions without hampering their output production. Obviously, efficient DMUs did not experiment any changes, thus their operational target match with their actual operating point.

3.4.

LCIA of target DMUs

Once the target values were obtained through DEA for the inefficient WWTPs, a new environmental impact assessment with LCA was performed. As in the previous environmental characterisation (Section 3.2), ReCiPe midpoint and endpoint hierarchist assessment methods were used. This new iteration allowed the quantification of the environmental burdens of inefficient plants if they were operated in an efficient way. Finally, it should be noted that even though the target DMUs are all under efficient conditions, the environmental performance of each unit is not the same due to differences in the target input inventories. The final stage of the five-step LCA þ DEA methodology consisted in the comparison between the environmental loads calculated for the current DMUs and those calculated for the virtual ones. In other words, the goal of this final stage is to prove that reductions in environmental impacts are directly related with increased eco-efficiency due to operational
zquez-Rowe et al., 2010). As plotted in Fig. 5, benchmarking (Va the single score endpoint environmental impact per cubic meter of treated water (i.e. per FU) for the original WWTPs (dark tone) was compared to the one associated with the virtual WWTPs (light tone). As expected, the environmental impacts in

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the virtual targets were lower than the ones of the original DMUs due to the minimisation of resource consumption.

4.

Discussion

4.1. Environmental and operational performance of Spanish WWTPs The results obtained were in accordance with previous results reported in LCA studies dealing with WWTPs performance, at least in terms of eutrophication potential (EP) and climate change (CC). In the case of the CC category, the average impact resulted in 4.5E-01 kg CO2 eq/m3; similar results were found in Rodrı´guez-Garcia et al. (2011a), with values for the global warming potential (GWP) category ranging from 2.0E-01 to 4.5E-01 kg CO2 eq/m3 or in the studies conducted by Pasqualino et al. (2009, 2011). Although the outcome of the present work matches the upper limit of the cited study it should be noted that the results were obtained throughout different assessment methodologies. Hence, even though they are not fully comparable, they can be used as a reference. Regarding Freshwater EP, the average outcome for this category was 2.16E-03 kg P eq/m3, while in Rodrı´guez-Garcia et al. 3 3 (2011a) was 1.42E-2 kg PO3 4 eq/m (4.63E-03 kg P eq/m ). As previously mentioned in Section 3.1, the range of Small WWTPs presented a higher variability in the LCI values due to its heterogeneity and, to a certain extent, due to lack of quality as compared to the other two groups within its data. As expected, the results of the LCIA showed similar levels of uncertainty, suggesting that small WWTPs are going to be strongly influenced by a series of operational factors. In contrast, the two other ranges appeared to have more consistent results, although standard deviations remained relatively high. As highlighted in previous studies, the LCA þ DEA combined methodology allows dealing with this type of heterogeneous datasets, allowing an analysis that goes
zquez-Rowe et al., beyond the use of average inventories (Va 2012). Despite the single score perspective presented in Fig. 4, and taking into consideration the discussion highlighted in Section 2.1, the EP impact avoided by treating the wastewater is the main function of a WWTP in an LCA study. Therefore, it was important to consider the NEB index in terms of EP as the output for the DEA matrix. The NEB average results for the three different ranges were: 3.86E-2 (±2.21E-2) kg N eq/m3 for Small WWTPs, 3.17E-2 (±2.16E-2) kg N eq/m3 for Medium WWTPs and 2.76E-2 (±2.26E-2) kg N eq/m3 for Large WWTPs. No negative results were observed, thus in terms of eutrophication potential the treatment hypothesis is always better than discharging the influent without any treatment for all DMUs. However, in terms of efficiency, the results computed in the DEA study showed that most of the DMUs under assessment ranged between 25% and 75%. In fact, only 11 DMUs of the 113 evaluated were found fully efficient (see Section 3.3), which represents a relatively low number in comparison with
zquez-Rowe et al., 2012). previous LCA þ DEA studies (Va Nevertheless, it should be noted that previous studies did not focus on WWTPs, but have dealt with other production

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Fig. 5 e Single score endpoint environmental impact (ReCiPe endpoint H) for original DMUs (dark tone) and virtual targets (light tone) per DMU for small (red) medium (blue) and large (green) wastewater treatment plants e WWTPs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

systems with their particular characteristics. In fact, it must be noted that WWTPs constitute complex systems influenced by several underlying factors that are analysed in depth in the following subsection. For instance, the size of the plant, the characteristics of the influent, climate region, type of technology, age of the facility and operational practices are some of the main driving forces influencing the efficiency of a WWTP. Some important differences were found between the three operational size ranges under study. The matrix for small WWTPs was composed of a total of 77 DMUs. The average efficiency of these units was 31.6%, the lowest of the three different clusters considered. Moreover, only five of these entities were operating at full efficiency. Interestingly, three of these units were designed only for carbon removal, while the other two operated with a technology allowing carbon and nitrogen removal. Regarding the medium WWTPs, the DEA matrix computed data from 14 DMUs, with the average efficiency calculated at 49.3%. In this case, three plants were deemed efficient, all of which were equipped with carbon and nitrogen removal technologies. Finally, in the large WWTPs range the average efficiency of the 22 DMUs assessed was 51.6%, the highest of the three groups assessed. Once again,

three WWTPs achieved full efficiency, two of them with carbon and nitrogen removal and the third with carbon removal technologies. In order to establish a possible benchmark for each range of p.e, the average of the inputs and output for the efficient WWTPs (i.e. F ¼ 100%) were calculated as shown in Table 5.

4.2.

Underlying factors

4.2.1.

Characteristics of the influent

It is well known that the influent composition is a strong driving force, influencing the performance of WWTPs (Pai et al., 2011). Furthermore, the organic load of the wastewater entering the plant constitutes a key parameter for its operation (Garnier et al., 2013). Highly loaded organic wastewater will ultimately lead to higher energy consumption due to increased aeration needs and higher sludge production rates. Similarly, diluted influents also constitute a potential cause of operational problems such as low sludge settling index (causing washout of biomass from the biological reactor) or low organic removal rates. In order to study the influence of the influent load on the eco-efficiency of the WWTPs, the range of Large WWTPs was

Table 5 e WWTPs benchmarking. Average inputs and output values for efficient DMUs. Range of WWTP

Small Medium Large

Inputs

Output

Electricity (kWh/m3)

Chemical consumption (Pt/m3)

Sludge production (kg sludge/m3)

NEB (kg N eq/m3)

8.97E-1 3.59E-1 2.81E-1

8.79E-4 5.96E-4 2.62E-3

5.76E-1 9.28E-1 4.43E-1

5.48E-2 4.69E-2 2.80E-2

w a t e r r e s e a r c h 6 8 ( 2 0 1 5 ) 6 5 1 e6 6 6

selected due to the consistency and trustworthiness of its data. The average of BOD5 for the three fully efficient DMUs (Numbers: 66, 82 and 97) within this range was 314.8 g O2/m3. If we take a look at the DMUs with the highest loaded influents (i.e., DMUs 52, 70 and 107), their BOD5 averages 669.6 g O2/m3, while their efficiencies are 65.9%, 17.9% and 40.8%, respectively. In contrast, the DMUs with lowest influent load (i.e., DMUs 57, 74 and 94) averaged 156. 5 g O2/m3. These resulted in efficiencies of 49.0%, 36.1% and 58.4%, respectively. When analysing these figures, it was not possible to identify a clear tendency relating the WWTP efficiency with the influent load. Nevertheless, Fig. 6 shows the relation between the influent EP and the eco-efficiency score for all the DMUs. Again, no obvious trend is observed, but it seems that the most ecoefficient WWTPs are comprised within a range (4E-02 and 9E-02 kg N eq/m3). Beyond this range, the scores obtained tend to be poor. Consequently, as expected, the environmental performance of a WWTP is probably not influenced by just one factor, but by a complex set of underlying parameters. For instance, DMU 70 presented low removal efficiency, which combined with a high loaded influent, resulted in a poor environmental performance.

Oversized plants normally will lead to unnecessary investment and operational costs, while undersized ones will require costly upgrades in order to fulfil environmental standards that also tend to vary through time. Regarding the 113 plants under assessment, the availability of the p.e design numbers and the calculated real p.e based on the 2011 data allowed the estimation of the deviation between these two parameters. As a result, an important amount of oversized and, to a lesser extent, undersized plants, were identified. In fact, 71 out of the 113 (63%) plants were found to be operating below 50% of their design capacity and, therefore, were deemed oversized. These figures resulted much more higher than the expected due to the normal design oversizing practiced by the engineers (seasonal population variations, climatic conditions, population growth, etc.). In the case of undersized facilities, only 8 operated above 120% of their design capacity. Nevertheless, as plotted in Fig. 6, no clear correlations between the overcapacity of the plants and their efficiency can be extracted. Only in the more-extreme cases (overcapacity > 300%) a clear tendency of low efficiency scores can be observed.

4.2.3. 4.2.2.

Characteristics of over and undersized WWTPs

WWTPs are usually built to have a long lifespan and, therefore, are considered rather static systems. Thus, when planning and designing these facilities, future uncertainty are usually not taken into consideration (Domı´nguez and Gujer, 2006). As a consequence of these bad practices when constructing WWTPs, many of these units tend to become in some cases over or undersized within a short period of time.

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Type of technology

Concerning the type of technology installed in each of the 113 WWTPs, no detailed data were available. Thus, a classification based on the secondary treatment technology, as provided in Rodrı´guez-Garcia et al. (2011a), was not possible. Nevertheless, the information available in the LCI of each DMU allowed dividing them in four main groups: i) plants designed only for carbon removal (C); ii) plants designed for carbon and nitrogen removal (CþN); iii) plants designed for carbon and

Fig. 6 e Influent eutrophication potential, population equivalent and overcapacity as compared to the eco-efficiency for Small WWTPs (blue diamonds), Medium WWTPs (red squares) and Large WWTPs (green triangles). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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phosphorous removal (CþP); and iv) plants with carbon, nitrogen and phosphorous removal (CþNþP). Finally, the carbon and phosphorous (CþP) group was neglected due to the low number (3 units) of facilities with this type of configuration. The aforementioned groups were analysed in the frame of the three WWTPs division previously set for this study (i.e., Small, Medium and Large WWTPs). The average efficiencies found after the analysis are the following: i) Small WWTPs: (C) ¼ 36.01%, (CþN) ¼ 29.88% and (CþNþP) ¼ 27.61%; ii) Medium WWTPs: (C) ¼ 31.0%, (CþN) ¼ 43.80% and (CþNþP) ¼ 69.40%; and, iii) Large WWTPs: (C) ¼ 51.0%, (CþN) ¼ 47.05% and (CþNþP) ¼ 58.75%. Taking into consideration these figures, it appears that simpler technologies/configurations tend to be more efficient for small WWTPs, despite the fact that the overall efficiencies achieved for this group are low in all cases. For medium and large WWTPs, efficiency was found to increase with more complex treatments. Therefore, in the latter, plants with nutrient removal perform better than plants with carbon removal technologies exclusively. The results for medium and large WWTPs were expected, as the chosen output in the DEA matrices focuses on eutrophication impact reduction, being the discharge of nutrients in the effluent the main contributors to this impact category. On the contrary, while the causes behind the inverted tendency in small WWTPs may be multiple, the operational management of these units, which is usually highly constrained due to lack of continuous supervision, could be an important factor to be taken into account.

4.2.4.

Climate region

An additional underlying factor worth analysing in detail is the climatic conditions that affect the different WWTPs. Therefore, four main climate regions were defined in Spain: Atlantic (>1000 mm and 14  C), Mediterranean (400e700 mm and 16  C), continental Mediterranean (400e600 mm and 15  C) and dry Mediterranean (<300 mm and 17  C). Rainfall and average temperature (in brackets) are considered the factors with greatest impact on the performance of a WWTP. For this specific parameter, a study relating efficiency and climate was only feasible for Small WWTPs, since a larger sample size allowed extracting some results. Thus, when efficiency was assessed, the facilities located in the Atlantic climate (11 DMUs) presented the highest efficiency average (47.6%), followed by continental Mediterranean (19 DMUs and an average efficiency of 33.6%) and Mediterranean ones (41 DMUs and 28.3%). Finally, dry Mediterranean ones, for which only 6 DMUs were available, showed a mean efficiency of 15.5%. Despite the difficulty to extract clear conclusions from these results, the fact that the Atlantic region resulted as the most favourable in terms of eco-efficiency, appears to be linked to the combination of mild temperatures and low loaded influents prone to less sludge production and reduced energy consumption due to low dissolved oxygen requirements.

4.3.

including WWTPs with tertiary treatment for disinfection purposes within the DEA matrices. In fact, a first iteration of the results was performed with this perspective, as shown in Table 6, demonstrating that the efficiency as defined in this study does not allow integrating these WWTPs with those that limit their treatment to the removal of organic matter and nutrients. Consequently, a significant difference was observed between the two types of treatment plants, regardless of the size of the plants. The main reason for this is linked to the use of energy and chemicals in the tertiary treatment stage, a process that despite its advantages in terms of disinfection and elimination of pathogens and microorganisms, does not contribute to a further reduction of eutrophication impacts, the output to which all the operational inputs are referred to in this study. In other words, the final water quality provided by the two types of WWTPs was not found to be comparable under the parameters that were taken into account in the assessment. Once this loophole in the interpretation of results was identified, a disaggregation of energy and chemical use inputs per treatment was suggested, in order to limit the system boundaries of all WWTPs to the effluent of the secondary treatment. However, while this approach was feasible for the use of chemicals, the energy consumption per plant was not possible to be disaggregated per treatment phase in most WWTPs. In addition, given the lower number of WWTPs applying a tertiary treatment within the entire sample, it was not feasible to run a separate LCA þ DEA simulation for these plants since the final number of DMUs was below the recommended levels in DEA studies (Cooper et al., 2007).

Inclusion of tertiary treatment in the matrices

4.4.

A series of statistical analysis were performed on the LCA þ DEA results obtained in the current study. In the first place, it is important to take into consideration the fact that a high level of uncertainty should be assumed linked to the selection of the assessment method. On the one hand, the mathematical assumptions of the ReCiPe assessment method lead to final characterization values with variable ranges of uncertainty, depending on the impact categories (Hauschild et al., 2013). Having said this, currently, ReCiPe constitutes one of the most up to date assessment methods, with a comprehensive selection of impact categories linked to the three damage categories commonly used in LCA studies:

Table 6 e Average efficiency values and standard deviation per WWTP group size when including WWTPs with tertiary treatment in the DEA matrices. Parameters

Small WWTPs

Medium WWTPs

Large WWTPs

TERa No TER TER No TER TER No TER Sample size (#) Efficiency (%) Standard deviation a

An important source of analysis and discussion was done within the scope of the study linked to the appropriateness of

Statistical and sensitivity analysis

19 25.3 15.1

77 31.6 22.6

5 20.0 11.1

14 49.3 32.8

6 39.4 34.3

22 51.6 24.7

TER, refers to facilities with tertiary treatment step (disinfection) in their water line.

w a t e r r e s e a r c h 6 8 ( 2 0 1 5 ) 6 5 1 e6 6 6

human health (HH), ecosystem quality (Ec) and resources (Re). On the other hand, the choice of weighting for the different damage categories (i.e., 40% for Ec; 30% for HH and 30% for Re), also creates a set of uncertainties that deserve further attention. Consequently, the MIXTRI 2.0 model, developed by Doka (2011), was used to illustrate how different weighting combinations can steer the final single score results used in the current study (Hofstetter et al., 1999). More specifically, the average unweighted scores per damage category for each matrix were modelled for the current and target average DMUs, in order to detect the best weighting zones between the three WWTPs groups. The results of conducting the MIXTRI assessment are depicted in Fig. 7. Fig. 7 confirms the improved average performance of large WWTPs in their current operating behaviour, regardless of the weighting of the different damage categories, although these results are not significant with a level of significance set at 35%. However, when the same computation is carried out for the target average operating values of the three matrices, the results indicate that when all entities are operating at full efficiency, there is an insignificant dominance of medium and small entities in different zones of the mixing triangle. Therefore, it is feasible to assume that if medium and small WWTPs were to be operated under similar management conditions as larger WWTPs, the optimization of inputs would lead to reduced environmental impacts and higher eco-efficiency levels. However, this conclusion should be managed with care, not only due to the uncertainties underlying these results, but also due to a series of social and economic factors. For instance, steering policy support to design smaller WWTPs could increase investment costs substantially and increase social unrest in areas where these plants are intended to be located (Molinos-Senante et al., 2013). Therefore, the interpretation of these results should be limited to efforts of improving the eco-efficiency of existing WWTPs. Finally, and in addition to the Mixing Triangle analysis, parametric analysis was performed in order to determine whether the average efficiency values for the three DEA matrices were significantly different. For this, an analysis of

663

variance (ANOVA) test was carried out for three-sample comparison. The one-way ANOVA showed that the values obtained for the three matrices were significantly different (ANOVA; p ¼ 0.001). Moreover, and for the sake of completeness, an additional ANOVA test was carried out including the samples for WWTPs with tertiary treatment (excluded from the main flow of the paper but discussed in detail in Section 4.3). In this case, the results between WWTPs with and without tertiary treatment also showed to be significantly different, which further encourages future studies to understand the eco-efficiency implications of disinfecting wastewater.

5.

Conclusions

An extensive analysis regarding the eco-efficiency of WWTPs throughout Spain has been performed in the current study. The results obtained present a scenario in which large inefficiencies are observed in the sample throughout different operational sizes, although higher efficiency levels were attained with increasing operational size. The reasons behind the poor efficiency levels are multiple, including climatic characteristics, the load of the influent or the level of complexity of the treatment technology. Interestingly, large WWTPs, despite their higher efficiency levels, which derived in lower minimisation potential opportunities, presented a poorer environmental profile than small or medium WWTPs when benchmarked. This behaviour suggests that smaller WWTPs, which unlike large WWTPs, lack continuous monitoring, have a relevant potential for improving their environmental profile if they were to benefit from stricter supervision. Despite the relevance of the results in terms of understanding differences between WWTPs or their potential for increasing their eco-efficiency and, therefore, their environmental profile from an integrated perspective, future research should focus on the interannual behaviour of the individual WWTPs, in order to determine if the yearly performance of these facilities follow a regular pattern or whether this variability is just as high as any of the parameters monitored in

Fig. 7 e Weighting triangle matrix for areas of environmental dominance for current (left figure) and target (right figure) average DMUs per WWTP size.

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the current study. For instance, the use of a Windows analysis model in DEA (Charnes et al., 1985; Asmild et al., 2004), in a
zquez-Rowe and similar way to the one performed by Va Tyedmers (2013), could provide important new inputs regarding the eco-efficiency of WWTPs.

Acknowledgements
zquez-Rowe wishes to thank the Galician GovernDr. Ian Va ment for financial support (I2C postdoctoral student grants programme). The authors acknowledge the financial support of the AQUAENVEC project (LIFE10 ENV/ES/000520). The authors from the University of Santiago de Compostela belong to the Galician Competitive Research Group GRC 2013-032, programme co-funded by FEDER.

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2014.10.040.

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