Wastewater treatment plants (WWTPs) have positive and negative impacts on the environment. Therefore, life cycle impact assessment (LCIA) can provide a more holistic framework for performance evaluation than the conventional approach. This study added water footprint (WF) to LCIA and defined ϕ index for accounting for the damage ratio of carbon footprint (CF) to WF. The application of these innovations was verified by comparing the performance of 26 WWTPs. These facilities are located in four different climates in Iran, serve between 1,900 and 980,000 people, and have treatment units like activated sludge, aerated lagoon, and stabilization pond. Here, grey water footprint (GWF) calculated the ecological impacts through typical pollutants. Blue water footprint (BWF) included the productive impacts of wastewater reuse, and CF estimated CO2 emissions from WWTPs. Results showed that GWF was the leading factor. ϕ was 4–7.5% and the average WF of WWTPs was 0.6 m3/ca, which reduced 84%, to 0.1 m³/ca, through wastewater reuse. Here, wastewater treatment and reuse in larger WWTPs, particularly with activated sludge had lower cumulative impacts. Since this method takes more items than the conventional approach, it is recommended for integrated evaluation of WWTPs, mainly in areas where the water–energy nexus is a paradigm for sustainable development.

  • An integrated method was developed for comparing WWTPs' performance.

  • Grey and blue water footprints were added to LCIA as environmental indices.

  • Method applicability was verified by comparing 26 WWTPs.

  • Larger WWTPs with activated sludge comparatively had less environmental impacts.

  • Wastewater treatment and reuse reduced 92% of environmental damage.

  • A new index for water–energy nexus in WWTPs was introduced.

Water–energy nexus (WEN) is one perspective under sustainable development that highlights the interrelations between water and energy production in policies or systems for their secure application (Wilson et al. 2021). Mini hydropower plants (Comino et al. 2020) and growing energy crops (Pacetti et al. 2015) are two examples that need water to produce clean energy and limiting greenhouse gas (GHG) emissions. On the opposite, desalination and wastewater treatment plants (WWTPs) require energy and emit GHGs for clean water supply (Chen et al. 2020). Accordingly, WWTP is mainly classified as energy consuming unit for water production (Kurian et al. 2019; Haitsma Mulier et al. 2022), in which its reclaimed water can ultimately reduce water-energy consumption. For example, a survey in the Sahara showed that wastewater reuse for irrigation could decrease 49% groundwater abstraction and 15% energy required for food production by reducing groundwater pumping demands (Ramirez et al. 2021). Based on the WEN perspective, in a semi-arid area like Iran, where energy sources are abundant but 80% of renewable water resources have been withdrawn (Madani et al. 2016), WWTPs are influential infrastructures with possible added values on a local scale (Jamshidi 2019).

WWTPs are relatively similar to factories that produce water and other products, like fertilizers and recovered phosphorous (Mo & Zhang 2013; Mulchandani & Westerhoff 2016). These facilities use energy for operating electro-mechanical tools, e.g. pumps, aerators, and mixers (Gu et al. 2017). Energy consumption in WWTPs indirectly emits GHG, while endogenous decay of carbonaceous and nitrogenous compounds directly releases carbon dioxide (CO2) and nitrous oxide (N2O) into the atmosphere (Kampschreur et al. 2009; Daelman et al. 2015; Huang et al. 2020). Karnaningroem & Anggraeni (2021) have argued that chemical residues and energy consumption are important factors in treatment plants. Thus, water quality should not be a single objective for their performance evaluation (Karnaningroem & Anggraeni 2021). The long-term impacts of (1) GHG emissions to the atmosphere, and (2) remaining pollutants in the effluent on the land ecosystem and health can be environmentally critical, particularly when they are dependent on WWTPs' operation (Sabeen et al. 2018; Yoshida et al. 2018). In other words, sustainable development goals (SDGs) including good health (SDG 3), clean water and sanitation (SDG 6), climate action (SDG 13), and life on land (SDG 15) are equally essential and they should be addressed simultaneously for WWTPs (Delanka-Pedige et al. 2021; Obaideen et al. 2022). From this point of view, operating WWTPs has both positive and negative environmental impacts (Gómez-Llanos et al. 2020). Nevertheless, there is a lack of integrated methods for evaluating the overall performance of WWTPs regarding their total impacts on the environment. WWTPs are currently reviewed for their water pollution removal or meeting regional water quality standards. That is why a new holistic approach is required for their performance evaluation considering possible environmental impacts instead of conventional pollution removal assessments. In the last decade, life cycle assessment (LCA) has been recommended to consider a broader range of impacts and compare the performance of WWTPs (Corominas et al. 2020).

LCA is a four step analytical tool that aggregates the estimated direct and indirect environmental impacts of activities or productions from their ‘cradle to grave’ (Hauschild et al. 2018). In its third step, life cycle impact assessment (LCIA), quantitative indices convert pollution loads or resource depletion into equivalent damages under different categories (Rosenbaum et al. 2018). For example, in ReCiPe 2016, a developed LCIA database, some affected midpoint categories for WWTPs are eutrophication, water depletion, and global warming (Gallego-Schmid & Tarpani 2019), while endpoints are the ecosystem, human health, and resources (Huijbregts et al. 2017). The indicator-based LCIA has some privileges. It firstly reduces the dependency of environmental impact assessments on expert opinions and puts one step toward an unbiased standard framework. Second, it unifies different environmental categories and aggregates their impacts that basically cannot be accumulated due to their different units, effective periods, and receiving bodies (e.g. air, marine, freshwater, agricultural or industrial soil) (Bulle et al. 2019). Finally, it has the flexibility to include more indices, e.g. water footprint (WF), in its calculations (Jamshidi & Naderi 2023a). On the contrary, a drawback of LCIA for evaluating WWTPs is that this method mainly quantifies the impacts of hazardous materials (e.g. toxins, heavy metals, and pharmaceuticals) in association with pollutants with clear ecological consequences (e.g. nitrogen and phosphorous in eutrophication). Regular water quality parameters in wastewater treatment like biochemical oxidation demand (BOD), total suspended solids (TSS), and chemical oxidation demand (COD) are not included in LCIA databases (Bai et al. 2017). Suryawan et al. (2021) recently considered the potential impacts of COD and BOD under eutrophication for the LCA of WWTPs (Suryawan et al. 2021). However, it is obvious that these parameters do not lead to eutrophication, as nitrogen and phosphorous are their main cause (Schindler et al. 2016; Jamshidi & Naderi 2023b). Another, Malik et al. (2015) proposed an environmental performance index (EPI) for comprehensive performance analysis of wastewater treatment (Malik et al. 2015). EPI can consider average treatment level, volume, connections etc. on national or regional scale but it is not applicable for specific WWTP evaluation. Therefore, we propose an integrated method to introduce WF into LCIA to fill this gap and enable WWTP evaluation via LCIA based on typical wastewater quality parameters.

WF is the water embedded in a production or service. It consists of blue, green, and grey elements (Hoekstra et al. 2011). Grey water footprint (GWF) is the key WF component for the performance evaluation of operating WWTP (Gómez-Llanos et al. 2020) because it represents the equivalent volume of freshwater required to assimilate pollutants to standard water quality levels (Franke et al. 2013). Moreover, WWTPs can recycle water for reuse. Based on this perspective, WWTPs are a resource for blue water footprint (BWF). These two factors can be adopted as consumed water in the LCIA of WWTPs (Morera et al. 2016). Carbon footprint (CF) has also the potential to join GWF and BWF for the evaluation of WWTPs. CF summarizes the total equivalent GHG emitted from a production or service like wastewater treatment. According to 225 WWTPs in China, it has been estimated that the direct and indirect carbon emissions from WWTPs constitute 64 and 36% of total CF, respectively (Chen et al. 2023). Here, the type of treatment unit, its design and operation, sludge management, the performance and efficiency of mechanical equipment, technologies used in WWTPs, the type of consumed fuel for energy or equipment production in their value chain, nutrient removal, and wastewater reuse are effective factors on CF (Parravicini et al. 2016). For the latter, researchers realized that wastewater treatment for irrigation can save 7% of energy and reduce 3% of the CF of WWTPs (Marinelli et al. 2021).

This study develops a joint method that adopts GWF in the LCIA to primarily allow this method to take typical wastewater treatment pollutants (e.g. COD, TSS) in calculations. Second, BWF and CF are added to include the impacts of wastewater reuse and GHG emissions in WWTPs evaluation. Finally, for verification, the developed method quantitatively evaluates and compares the overall performances of 26 WWTPs in Iran during operation. The research scope is confined to biological wastewater treatment and excludes the possible impacts of construction, sludge management, chemical additives, and wastewater collection. Therefore, this research is basically different in methodology and basic assumptions from the common LCA studies about WWTPs. It aims to develop a holistic framework for the performance evaluation of WWTPs. This method estimates the equivalent environmental damages, instead of the conventional approach that compares pollution removals. This study also discusses how to include typical pollution loads (BOD, COD, and TSS), quality standards, wastewater reuse, and possible carbon emission within GWF, BWF and CF for accounting for the related damages in LCIA. Consequently, the method puts one step toward simplifying further integrated WWTP evaluations on the basis of WEN thinking.

This study used a three-step method in the calculation. At first, water quality data in the inlet and outlet of WWTPs was analyzed and used for accounting for the environmental footprints (GWF, BWF, and CF). Second, midpoint and endpoint indices (based on ReCiPe database) converted pollution loads and footprints to equivalent cumulative damages in LCIA. Finally, the performance of WWTPs was evaluated and compared based on the developed water-energy index and quantified damages. Figure 1 shows the overall methodology and its steps.
Figure 1

The overall method of this research with its steps.

Figure 1

The overall method of this research with its steps.

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Study area

According to the official report of the Iranian National Water and Wastewater Company, more than 35.2 million inhabitants in cities and rural are connected to the engineered sewage systems with about 250 operating WWTPs (NWWC 2021). These facilities mostly use activated sludge (AS), waste stabilization ponds (SP), and aerated lagoon (AL) for BOD, COD, and TSS removal from domestic wastewater. The aggregate operating capacities of these three processes in Iran consist of about 65, 15, and 13% of the collected wastewater, respectively. The remaining capacity is attributed to other systems, like sequencing batch reactors (WRI 2020).

This research uses the average water quality data (2015–2021) of the influent and effluent of 26 WWTPs located in central Iran, in Esfahan (Yazdian & Jamshidi 2021), Chaharmahal and Bakhtiary, and Markazi provinces (Jamshidi et al. 2023). These WWTPs are spatially distributed among four climates (Figure 2) and currently treat the municipal wastewater of 23 cities with 1,900–980,000 population-equivalents (PE). Three types of biological treatment units, AS, AL and SP are used and there are different regional demands for recycling their treated wastewater (Table 1). We also classified the capacity of WWTPs in accordance with Goliopoulos et al. (2022), in which small WWTPs receive an inflow of PE ≤50,000, whereas large WWTPs serve a higher (>50,000 PE) population (Goliopoulos et al. 2022). For comparing pollution removals in studied WWTP, we also used box plots and analysis of variance by Minitab 19 software.
Table 1

The specifications of studied WWTPs

WWTPsIDProcessPopulationFlow (m3/d)ClimateReuse alternative
Ardestan W1 SP 42,105 955 Arid Irrigation /aquifer recharge 
Naeen W2 SP 39,261 3,045 Arid Irrigation /aquifer recharge 
Anarak W3 SP 1,903 274 Arid Irrigation /aquifer recharge 
Kuhpayeh W4 SP 23,674 1,026 Arid Irrigation /aquifer recharge 
Harand W5 AL 8,455 1,140 Arid Irrigation /aquifer recharge 
Varzaneh W6 SP 29,718 2,493 Arid Irrigation /river rehabilitation 
Borujen W7 AS 57,071 7,370 Humid Irrigation /industrial application 
Buyin W8 SP 24,163 1,432 Humid Irrigation /aquifer recharge 
Daran W9 AL 20,078 1,563 Humid Irrigation /aquifer recharge 
Shahrekord W10 AS 190,441 27,700 Humid Irrigation /industrial application 
Baghbahadoran W11 AL 10,279 1,144 Mediterranean Irrigation 
ZarrinShahr W12 AL 55,817 9,638 Semi-arid Irrigation 
Semirom W13 AL 74,109 2,636 Mediterranean Irrigation 
Baharestan W14 AS 79,023 13,046 Semi-arid Irrigation 
E. Isfahan W15 SP 490,315 54,238 Semi-arid Irrigation /river rehabilitation 
N. Isfahan W16 AS 980,630 181,028 Semi-arid Irrigation /aquifer recharge 
FooladShahr W17 SP 88,426 12,523 Semi-arid Irrigation /industrial application 
Ghahderijan W18 AL 34,226 1,139 Semi-arid Irrigation 
S. Isfahan W19 AS 653,753 106,399 Semi-arid Irrigation /river rehabilitation 
Delijan W20 SP 40,902 8,400 Semi-arid Irrigation /industrial application 
SepahanShahr W21 AL 70,557 12,088 Semi-arid Irrigation /aquifer recharge 
ShahinShahr W22 AS 352,001 42,994 Semi-arid Irrigation /industrial application 
Shahreza W23 SP 159,797 7,773 Semi-arid Irrigation 
Safayieh W24 AS 50,137 1,846 Semi-arid Irrigation /industrial application 
Mobarakeh W25 AL 150,411 4,458 Semi-arid Irrigation /industrial application 
Najafabad W26 AL 319,205 4,436 Semi-arid Irrigation /industrial application 
WWTPsIDProcessPopulationFlow (m3/d)ClimateReuse alternative
Ardestan W1 SP 42,105 955 Arid Irrigation /aquifer recharge 
Naeen W2 SP 39,261 3,045 Arid Irrigation /aquifer recharge 
Anarak W3 SP 1,903 274 Arid Irrigation /aquifer recharge 
Kuhpayeh W4 SP 23,674 1,026 Arid Irrigation /aquifer recharge 
Harand W5 AL 8,455 1,140 Arid Irrigation /aquifer recharge 
Varzaneh W6 SP 29,718 2,493 Arid Irrigation /river rehabilitation 
Borujen W7 AS 57,071 7,370 Humid Irrigation /industrial application 
Buyin W8 SP 24,163 1,432 Humid Irrigation /aquifer recharge 
Daran W9 AL 20,078 1,563 Humid Irrigation /aquifer recharge 
Shahrekord W10 AS 190,441 27,700 Humid Irrigation /industrial application 
Baghbahadoran W11 AL 10,279 1,144 Mediterranean Irrigation 
ZarrinShahr W12 AL 55,817 9,638 Semi-arid Irrigation 
Semirom W13 AL 74,109 2,636 Mediterranean Irrigation 
Baharestan W14 AS 79,023 13,046 Semi-arid Irrigation 
E. Isfahan W15 SP 490,315 54,238 Semi-arid Irrigation /river rehabilitation 
N. Isfahan W16 AS 980,630 181,028 Semi-arid Irrigation /aquifer recharge 
FooladShahr W17 SP 88,426 12,523 Semi-arid Irrigation /industrial application 
Ghahderijan W18 AL 34,226 1,139 Semi-arid Irrigation 
S. Isfahan W19 AS 653,753 106,399 Semi-arid Irrigation /river rehabilitation 
Delijan W20 SP 40,902 8,400 Semi-arid Irrigation /industrial application 
SepahanShahr W21 AL 70,557 12,088 Semi-arid Irrigation /aquifer recharge 
ShahinShahr W22 AS 352,001 42,994 Semi-arid Irrigation /industrial application 
Shahreza W23 SP 159,797 7,773 Semi-arid Irrigation 
Safayieh W24 AS 50,137 1,846 Semi-arid Irrigation /industrial application 
Mobarakeh W25 AL 150,411 4,458 Semi-arid Irrigation /industrial application 
Najafabad W26 AL 319,205 4,436 Semi-arid Irrigation /industrial application 
Figure 2

Location and spatial distribution of studied WWTPs in different climates.

Figure 2

Location and spatial distribution of studied WWTPs in different climates.

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Water footprint

Since GWF is the embodied water for pollution assimilation, it should be accounted for based on the net pollution discharged from a manufacturer, e.g. industry or agriculture (Roudbari et al. 2023). Nevertheless, WWTP is normally recognized as a service that inherently reduces the pollution of wastewater and its performance assessment is reliant on its pollution removal. Regarding the new perspective, we can consider WWTP as a water factory. However, its net GWF from the inlet (raw wastewater) to the outlet (treated wastewater) is still negative (GWFin > GWFout). In order to solve the problem, we recommend accounting the GWFs twice based on the pollution loads in the inlet (GWFin) and outlet (GWFout). Here, the performance equals the GWF reduction (%) from the inlet to the outlet (Equation (1)). In addition, their corresponding environmental damages (see Equations (7) and (8)) should also be calculated twice in accordance with GWFin and GWFout. For multiple pollutants, GWF is the maximum value among water quality parameters (Equation (2)). It means that in WWTPs, GWFin can be reliant on a quality parameter (e.g. BOD), while GWFout can be dependent on another parameter (e.g. TSS). It does not necessarily require using identical water quality parameters for GWFs in the inlet and outlet. Equation (2) defines GWF factors (Tahar et al. 2018; Varol & Balcı 2020).
(1)
(2)

Here, GWF is the grey water footprint (m3/ca.) of a WWTP, C is the pollutant (i) concentration in inlet or outlet (mg/L), Q is the average flow rate (m3/yr) of wastewater, PE is the population-equivalent that represents the capacity of WWTPs (person), Cmax and Cnat are the allowable and natural concentration of pollutants (mg/L), respectively in t receiving environment as assumed in Table 2. For wastewater reuse (e.g. industries or irrigation), Cmax is different with discharge conditions. The consumer of reclaimed water is now responsible for its quality. This difference in Cmax departs the GWF of discharged or reused wastewater.

Table 2

Assumed Cmax and Cnat for GWF accounting in two scenarios

ParametersCmax (mg/L)
Cnat (mg/L)Reference
DischargeReuse
Total suspended solids (TSS) 50 100 10 DOE (2016)  
Chemical oxidation demand (COD) 20 60 
Biochemical oxidation demand (BOD) 10 30 
ParametersCmax (mg/L)
Cnat (mg/L)Reference
DischargeReuse
Total suspended solids (TSS) 50 100 10 DOE (2016)  
Chemical oxidation demand (COD) 20 60 
Biochemical oxidation demand (BOD) 10 30 

It is noteworthy that the effluent of WWTPs is fundamentally classified as blue water (Hoekstra et al. 2011). It means that WWTPs produce blue water (Jamshidi 2019) for a purposeful reuse or release to the environment. In both scenarios, the WFs should be calculated by Equation (3) which is not different to the main WF definition (GWF + BWF) (Hoekstra et al. 2011). It only emphasizes that BWF (Equation (4)) acts as a water resource or a replacement for water abstracts (m3/ca.).
(3)
(4)

In some cases, the WF of WWTPs may become negative (WF <0) where the effluent is well treated so that C<(Cmax– Cnat). On this condition, the WWTP does not embed water, it works as a water supply instead.

Carbon footprint

This study briefly reviewed the literature to estimate the average CF of WWTPs with respect to their conventional secondary treatment units. Accordingly, we took two assumptions about the main source of CF in WWTP, and the approximate CF of WWTP.

Energy utilization and GHG emissions from mechanically aerated (e.g. AS) or natural-based (e.g. SP) units are different. Here, we assumed that the CF of WWTPs is mainly dependent on the type of biological treatment units (Ramachandra & Mahapatra 2015; Wu et al. 2022). Maktabifard et al. (2020) based on 6 full-scale WWTPs concluded that energy source affects less than 50% of the total CF of WWTPs (Maktabifard et al. 2020), while direct GHG emission from biological treatment units has higher impacts. Later, Goliopoulos et al. (2022) concluded that aeration with secondary treatment units consumes more than 70% of energy in WWTPs (indirect GHG), whereas pretreatment uses only 10% (Goliopoulos et al. 2022). These two studies imply that if we exclude the related CF of sludge management, chemical additives, and construction materials, the majority of direct and indirect GHG emissions from operating WWTPs are attributed to the aeration and biological treatment unit. In Poland, India and England, less than 50% of the CF of WWTPs is due to its construction materials (Singh et al. 2016; Zawartka et al. 2020), but it is also noted that this ratio is reducing in operating WWTPs due to electricity consumption and direct CO2 emissions (Zawartka et al. 2020). According to a recent study on large Italian WWTPs, the endogenous decay of wastewater in secondary treatment units includes 52% of direct GHG emissions from WWTPs, whereas energy consumption takes 47% of indirect emissions (Riccardo et al. 2023).

According to the literature, the CFs of two WWTPs in Poland having AS with 250,000 and 60,000 PE range between 17 and 39 grCO2e/PE (Maktabifard et al. 2019). However, they recommended multiplying CF with 2–3 wherever N2O is also included for estimating GHG emissions. Hence, a relatively large WWTP (>50,000 PE) with conventional AS has CF >60 grCO2/PE. In Scotland, the annual average GHG emissions of 16 WWTPs were about 7–108 grCO2e/PE. It was mainly due to the pumps, excess sludge, and additives used for denitrification (Gustavsson & Tumlin 2013). In Spain, the average CF of 4 WWTPs was about 280 grCO2e/m3 including sludge management (Gómez-Llanos et al. 2020). In Greece, aeration for pretreatment and biological treatment has CF of about 120 grCO2e/PE (small WWTPs) and 60 grCO2e/PE (large WWTPs) with an average ±15% (10–20 grCO2e/PE) variation for pumps (Mamais et al. 2015). In Iran, it was also estimated that WWTPs with mechanical aeration have CF of about 30–36 grCO2e/PE (Aghabalaei et al. 2023). Therefore, we assumed that the average CF of secondary treatment units like AL, AS and SP are 110, 80 and 20 grCO2e/PE, respectively. These assumptions are in the range of the reviewed studies.

Life cycle impact assessment

ReCiPe 2016 is one LCIA model with a recently revised database (Huijbregts et al. 2017). Here, midpoint coefficients estimate the equivalent impacts in different categories, while endpoint coefficients convert these impacts into the major categories of human health and ecosystem (k). The damaged human health is based on disability-adjusted years (DALY), while the ecological impairments are based on the probable number of affected (PAF) species per year (Huijbregts et al. 2017). Table 3 shows the details of the applied midpoint and endpoint impact categories in this study. It should be noted that all impact categories of ReCiPe are not necessarily applicable to the performance evaluation of WWTPs. For example, Ionizing radiation and ozone depletion are not related to WWTPs and particulate matter (PM2.5) is not a typical measured pollutant. Yet, we categorized GWF as water consumption, while BWF is a water production index for WWTPs.

Table 3

Impact categories and the conversion coefficients of ReCiPe (Huijbregts et al. 2017)

Impact categoryMidpoint
Endpoint
Normalization factor (N)
Influencing factorCoefficient (M)UnitCoefficient (E)Unit
Human health GWF& BWF 0.5 m3 2.22 × 10−6 DALY/m3 1.96 × 10−4 
GHG 20-80-110a CO2-eq/ca. 9.28 × 10−7 DALY/CO2-eq. 7.42 × 10−3 
Terrestrial ecosystem GWF& BWF 0.5 m3 1.35 × 10−8 Species.year/m3 3.48 × 10−6 
GHG 20-80-110 CO2-eq/ca. 2.80 × 10−9 Species.year/ CO2-eq. 2.24 × 10−5 
Aquatic ecosystem GWF& BWF 0.5 m3 6.04 × 10−13 Species.year/m3 6.16 × 10−10 
GHG 20-80-110 CO2-eq/ca. 7.65 × 10−14 Species.year/ CO2-eq. 6.11 × 10−10 
Impact categoryMidpoint
Endpoint
Normalization factor (N)
Influencing factorCoefficient (M)UnitCoefficient (E)Unit
Human health GWF& BWF 0.5 m3 2.22 × 10−6 DALY/m3 1.96 × 10−4 
GHG 20-80-110a CO2-eq/ca. 9.28 × 10−7 DALY/CO2-eq. 7.42 × 10−3 
Terrestrial ecosystem GWF& BWF 0.5 m3 1.35 × 10−8 Species.year/m3 3.48 × 10−6 
GHG 20-80-110 CO2-eq/ca. 2.80 × 10−9 Species.year/ CO2-eq. 2.24 × 10−5 
Aquatic ecosystem GWF& BWF 0.5 m3 6.04 × 10−13 Species.year/m3 6.16 × 10−10 
GHG 20-80-110 CO2-eq/ca. 7.65 × 10−14 Species.year/ CO2-eq. 6.11 × 10−10 

aAssumed as section carbon footprint.

ReCiPe has three perspectives to evaluate LCIA indices (Tamburini et al. 2019). This study used the hierarchical perspective for both midpoint and endpoint indices as it is common for modeling and policy-making in developing countries (Jamshidi & Naderi 2023a). Accordingly, the studied environmental factors of WWTPs are initially converted into equivalent damages by Equation (5) (Jamshidi & Naderi 2023a).
(5)
where U is the midpoint index, T is the pollution load (kg/yr) or consumed water (m3/yr), M is midpoint conversion coefficient, and j is the affected category media (e.g. marine or terrestrial ecosystem). Afterwards, ReCiPe uses endpoint coefficients (E) regarding the corresponding human health or ecosystem damages (Table 3) to convert midpoint indices into endpoint index (V) by Equation (6). Thus, the environmental impacts with different units are now aggregated under a unified index as DALY (health) or PAF (ecological).
(6)
Endpoints (V) are normalized (Zk) per capita as Equation (7) for each major impact category (k) of human health or ecological damage.
(7)
(8)

Here, S is dimensionless which represents the cumulative impact (CI), N is the normalization factor that converts endpoint damages to equivalent impairment per capita, and W is the weight of major impact categories (Equation (8)). In this study, the weights (W) of human health and ecological damages are assumed 0.4 and 0.6, respectively, similar to the EPI method (Wendling et al. 2018).

By means of LCIA framework, we could define a state-of-the-art index (ϕ) that can account for the ratio of equivalent damages of GHG emission from WWTPs (SGHG) to the total impairments of WF (SWF) in these facilities (Equation (9)). Here, S is the CI of GHG and WF.
(9)
ϕ is dimensionless and represents the hidden damages of GHG emission from WWTPs on the basis of WF damages. For example, ϕ > 0.5 implies that the WWTP is more risky with its global warming than remained pollutants in wastewater. Conversely, ϕ < 0.5 highlights the prevailing consequences of wastewater pollution. ϕ < 0 shows that WWTP has synergy with the environment by recycling water (SWF<0) or clean energy production (SGHG<0). As noted before, the positive and negative impacts of wastewater pollution (GWF) and reuse (BWF) are already considered within WF.

Water footprint

According to the comparative performances of 26 WWTPs on pollution abatement derived from Minitab software (Figure 3), it can be concluded that AS was significantly (P-value < 0.05) more effective on BOD, COD, and TSS removal with 87.1 ± 2% (P = 0.014), 87.1 ± 1.4% (P = 0.002) and 86.3 ± 1.7% (P = 0.00) efficiency, respectively. The average BOD removal for AL and SP were 80.4 ± 2% and 79.5 ± 1.3%, respectively, and their COD removal were 76.7 ± 2.5% (AL) and 75.8 ± 1.9% (SP). However, AL and SP were less effective on TSS removal about 55.1 ± 7% and 54 ± 3.4%, respectively. In addition, WWTPs in arid climates were significantly less effective in COD (P = 0.022) and TSS (P = 0.014) removal in comparison with other climates, whereas BOD removal was not significantly different (P > 0.05).
Figure 3

Pollution removal (%) of WWTPs based on treatment units (a) and climates (b).

Figure 3

Pollution removal (%) of WWTPs based on treatment units (a) and climates (b).

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Reporting pollution removals follows the conventional method for the performance evaluation of WWTPs. GWF can instead take multiple pollutants simultaneously in evaluation combined with the size (PE) of WWTPs and the regional conditions (Cmax) for treated wastewater. For example, Figure 4(a) illustrates the calculated GWF of three pollutants (m3/ca) in the effluent of 26 WWTPs (Equation (2)). Figure 4(b) also re-evaluates these GWFs by wastewater reuse. These figures show that COD is mostly the leading factor in GWF in both scenarios; except for Borujen and Delijan WWTPs. Higher COD concentration (C) in the effluent and the range of defined variables (Cmax and Cnat) in Equation (2) are effective factors that highlighted COD as the leading pollutant in this study. On the contrary, TSS is not a significant pollutant (Figure 4(a)). In this approach, PE affects GWFs (m3/ca), wastewater reuse changes GWF by Cmax (see Table 2), and biological treatment units (AS, AL and SP) have different removals (see Figure 3). Thus, a multi-pollutant GWF is a more inclusive index for the performance evaluation of WWTPs than the conventional pollution removal assessments.
Figure 4

The grey water footprint of WWTPs’ effluent based on main pollutants (a) and with wastewater reuse (b).

Figure 4

The grey water footprint of WWTPs’ effluent based on main pollutants (a) and with wastewater reuse (b).

Close modal
Figure 5 demonstrates the overall GWF of each WWTP that equals the maximum GWF among 3 pollutants (see Equation (2)). The average GWF of all studied WWTPs equals 0.71 ± 0.09 m3/ca with the range of 0.08–1.57 m3/ca. In addition, these facilities supply blue water for irrigation or other purposes (see Table 1). The average produced BWF is 0.1 ± 0.01 m3/ca which ranges between 0.01 and 0.21 m3/ca. It is noteworthy that the quality of reused wastewater was previously included in GWF (see Table 2). Accordingly, wastewater reuse reduces the average GWF from 0.71 ± 0.09 m3/ca to 0.19 ± 0.02 m3/ca (73%) with the range of 0.02–0.43 m3/ca (Figure 5). This is due to the fact that treated wastewater is reused with more lenient water quality standards in comparison with direct discharge to the environment. As a consequence, the average ratio of embedded GWF to the produced BWF (GWF/BWF) by reuse decreases by 71% from 7.07 ± 0.45 to 2.05 ± 0.17 for 26 WWTPs (Figure 5). It implies that discharging treated wastewater from a moderate WWTP requires the blue water of at least seven identical WWTPs for its pollution assimilation, whereas, this rate reduces to 2 by reuse. The average WF of WWTPs reduces from 0.6 ± 0.08 m3/ca (discharge) to 0.094 ± 0.017 m3/ca (84%) for these facilities on the condition of continuous wastewater reuse.
Figure 5

The GWF and BWF of WWTPs and their ratios with and without wastewater reuse.

Figure 5

The GWF and BWF of WWTPs and their ratios with and without wastewater reuse.

Close modal

It should be noted that zero wastewater discharge into the environment is one recommended strategy (Tong & Elimelech 2016). However, LCA needs some reinforcements to quantify the impacts of this strategy. GWF, combined with BWF and CF, has the potential for bridging the analysis above with the environmental impacts and interpreting the cumulative environmental impairments.

Life cycle impact assessment

Figure 6 illustrates the ratio of equivalent environmental impairments of WWTPs on the basis of their WF and GHG emissions by the application of ReCiPe indices. It shows that WF is mostly the leading index. On average, the damages of GHG emissions from biological treatment units include 3.5% ± 0.95 of their accumulated environmental impairments. This ratio ranges between 0.21% (Anarak) and 19.2% (NajafAbad) among the WWTPs. If the benefits of wastewater reuse are included, the average increases to 6.2% ± 1.58. It implies that the ecological or health impacts of pollutants remaining in the treated wastewater are relatively higher than the impacts of GHG emissions from WWTPs.
Figure 6

The ratio of GHG and WF environmental impairment of WWTPs.

Figure 6

The ratio of GHG and WF environmental impairment of WWTPs.

Close modal
According to Equation (9), ϕ is the damage ratio of CF per WF in WWTPs (not total impairments). It equals 4% ± 1.14 for discharged treated wastewater and increases to 7.5% ± 2.29 by reuse. Here, the equivalent environmental damage of GHG emission per WF in WWTPs (ϕ) ranges between 0.2% (Anarak) and 23.8% (NajafAbad) due to the factors like the capacity of WWTPs (large/small), wastewater reuse, and type of treatment units (AS, AL, SP). For example, ϕ for systems with mechanical aeration (AS, AL) is seven folds greater than SP (Figure 7). ϕ is also comparatively 1.4 times greater for smaller WWTPs (<50,000 PE) than larger WWTPs (>50,000 PE). The last result, that smaller WWTP has higher CF, is due to the proportion of energy consumption per treated wastewater. Obviously, larger WWTPs require more energy, for their pumps or sludge aeration, but this consumption would not be necessarily doubled by doubling PE. Energy consumption in WWTPs is elevated step-by-step, by adding equipment in line, when PE linearly increases. Thus, the CF of a small WWTP is relatively significant as similarly verified by the research on 30 WWTPs in Greece (Goliopoulos et al. 2022). The estimated ratio of 1.4 was also obtained by their research as the ratio of GHG emission (CO2/ca.) of small to large WWTPs. Singh et al. (2016) concluded that the energy used in smaller WWTPs (KWh/m3) may even exceed 12 times that of large WWTPs (Singh et al. 2016).
Figure 7

Equivalent GHG damages per WF (ϕ) regarding WWTPs' units, reuse, and sizes. Greater ϕ shows higher impacts of GHG emission than water pollutants on the environment.

Figure 7

Equivalent GHG damages per WF (ϕ) regarding WWTPs' units, reuse, and sizes. Greater ϕ shows higher impacts of GHG emission than water pollutants on the environment.

Close modal

ϕ index implies that although aeration has advantages in improving treatment performance (see Figure 3 for AS), it has a secondary impact as GHG emission. In other words, WWTP uses energy to reduce pollution damage to human health and the ecosystem, whereas it adds some damage through indirect GHG emission. Hence, ϕ is an index for WWTPs to compare the impacts of energy consumption respecting the impacts of remaining pollution for a sustainable performance. In other words, ϕ is applicable in WEN studies for WWTPs, or similar facilities. However, its optimal value still requires more case studies. Currently, researchers refer to the water used for energy production for joining CF with WF. For instance, in China, researchers recently estimated that WWTPs require 41grCO2/m3 GWF reduced (Gu et al. 2016). However, our study joins CF and WF differently through ϕ and its average among 26 WWTPs is 4–7.5%. However, it can be increased, by about 2–10 folds (15–30%), if the CFs of sludge management, construction, nutrient removal, chemical additives, and collection systems are added (Parravicini et al. 2016; Zawartka et al. 2020).

ReCiPe by its coefficients provides a quantifiable tool to account for the cumulative environmental damages of wastewater discharge. Figure 8 shows the difference between the overall impairment of untreated, treated and reused wastewater for all studied WWTPs. Wastewater treatment and its reuse can reduce the average inlet CI from 5.05 ± 0.7 to 0.83 ± 0.11 (84%) and 0.42 ± 0.05 (92%), respectively. Previously, researchers claimed that wastewater treatment and reuse can yield a 33% reduction in the LCIA of WWTPs regarding its GHG emissions (Miller-Robbie et al. 2017).
Figure 8

The impacts of wastewater treatment and reuse on the cumulative environmental damages (CI).

Figure 8

The impacts of wastewater treatment and reuse on the cumulative environmental damages (CI).

Close modal
Combining WF with CF in LCIA can present a more holistic approach for comparing WWTPs' performance. Here, BOD, COD, and TSS removals are no longer separated evaluators. As an alternative, the likely impacts of multi-pollutants and treatment strategies determine the overall performance. According to Figure 9, it is realized that the wastewater reuse strategy (with/without reuse) is more effective on CI removal (%) than the capacity of WWTPs (small/large). Activate sludge has also comparatively higher performance, based on CI removal (%), than SP and AL.
Figure 9

Overall performance of WWTPs based on their units, reuse, and sizes. More CI removal shows higher performance by the developed method.

Figure 9

Overall performance of WWTPs based on their units, reuse, and sizes. More CI removal shows higher performance by the developed method.

Close modal

Finally, we should argue that the accuracy of CI is its main weakness and having access to the background databases of LCA may increase this accuracy for integrated performance evaluation of WWTPs in developing countries (Gallego-Schmid & Tarpani 2019). Other LCIA models, such as the Ecoinvent database, can be comparatively used to estimate the overall GHG emission and environmental impacts of WWTPs (Lahmouri et al. 2019). According to Yoshida et al. (2014), LCA highlights the impact categories responsible for total impairments. Eutrophication, ecotoxicity, and global warming are three main classifications (Yoshida et al. 2014). A recent study in Iran showed that ecotoxicity with global warming are critical impact category of a WWTP (Tayyebi et al. 2023). It should be noted that this study excluded reporting the related impacts of sludge, nutrients, construction, collection systems, and chemical additives due to their details. These factors can surely improve the developed method with a holistic perspective. Thus, we expect future case studies, in different locations, to follow the same method for evaluating the performance of WWTPs regardless of their spatial specifications. However, due to the details of their operation and impact categories (LCIA), the final results would be different. For example, it is probable that the performance of SP exceeds AS in other locations, or nitrogen becomes a pivotal pollutant for GWF. Yet, we mostly recommend this approach for application in areas where: (1) the government has concerns about WWTPs' sustainability, (2) the monitoring organization, like the Department of Environment, requires a holistic quantifiable index for reporting, and (3) engineers and companies seek for greener options rather than cost-effective alternatives.

This study primarily developed a quantitative holistic method by introducing WF (GWF–BWF), as a water consumption factor, and CF, as a GHG emission index, in the LCIA of WWTPs. Its application was also verified for comparing the overall performances of 26 WWTPs, in different climates with dissimilar sizes and secondary treatment units. The developed method considers more items with a broader impact assessment perspective for performance evaluation of WWTPs, rather than the conventional pollution removal assessments. Its advantages are: (1) including wastewater reuse, (2) simultaneous multi-pollutant assessment, and (3) considering the net impacts from the inlet to the outlet. These advantages are obtained by adding BWF and GWF in LCIA for cumulative impact assessment. Here, GWF combines multiple pollutants and facilitates complex pollution removal assessments. It also has a water consumption theme that can be easily used in LCA, like BWF. Therefore, this method is recommended for future evaluations, particularly for WWTPs in areas where policy-makers or engineers are seeking sustainable options. Its application is not confined by pollutants, treatment systems, operation, climate conditions or locations. These factors affect indices or midpoints which eventually convert to cumulative normalized damages. Therefore, the developed method can support evaluators with quantified indices for a more inclusive comparison of different WWTPs.

This study also concludes the following:

  • For studied WWTPs, GWF played a critical role in the cumulative impacts under LCIA and COD was the major pollutant. The related damages of GHG emissions, from endogenous decay and energy consumption in secondary treatment units, constituted 3.5–6.2% of total impairments. In addition, the average ratio of GWF to BWF was from 2 to 7. It implied how much embedded reclaimed water was required for assimilating the remained pollutants in treated wastewater. GWF/BWF ratio is applicable for reporting the impact of wastewater reuse with respect to its remained pollution content.

  • Wastewater treatment and reuse could reduce the average cumulative impacts (CI) of raw wastewater by 92% for studied WWTPs from 5 to less than 1. These CIs implied that, on average, each citizen deteriorated the environment more than 5 folds of his share (footprint) by discharging raw wastewater. WWTPs with reuse could reduce this rate to about 0.4 < 1.

  • Among studied WWTPs, AS was a relatively cleaner treatment unit compared to AL and SP. Larger WWTPs had moderately less CI than smaller WWTPs. Here, reclamation and reuse played a critical role in CI reduction in comparison with treatment units and capacity. Accordingly, policy-makers in the studied area can now plan for upgrading WWTPs if sustainability and nexus thinking become mandatory. It is also noteworthy that the proposed method can have different performance results for WWTPs in other areas, as a matter of operation, pollutants, reuse strategies, climatic conditions, standardization, and LCIA impact categories. However, it does not reject the applicability or validation of the proposed method.

  • ϕ was a new index calculated by the developed method. It demonstrated the equivalent GHG damages of WWTPs in proportion to the WF impairments about 4–7.5%. However, further case studies are required to elaborately determine this ratio by considering other items in WWTPs. Sludge management, construction materials, chemical additives, nutrient removal, wastewater collection systems, etc. can change this ratio.

Authors should thank Dr Hamed Yazdian (University of Isfahan), Chaharmahal Bakhtiari Water and Wastewater Co. (contract no. 4940.44.S), and Markazi Water and Wastewater Co. (contract no. 11713.1.Q), for their support on giving access to the raw data of studied WWTPs.

S.J. was involved in conceptualization, visualization, supervision, methodology, formal analysis, investigation, and writing. M.F. performed methodology and did formal analysis. H.M. performed methodology and did the investigation.

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

The authors declare there is no conflict.

Aghabalaei
V.
,
Nayeb
H.
,
Mardani
S.
,
Tabeshnia
M.
&
Baghdadi
M.
2023
Minimizing greenhouse gases emissions and energy consumption from wastewater treatment plants via rational design and engineering strategies: A case study in Mashhad, Iran
.
Energy Rep.
9
,
2310
2320
.
https://doi.org/10.1016/j.egyr.2023.01.017
.
Bulle
C.
,
Margni
M.
,
Patouillard
L.
,
Boulay
A.-M.
,
Bourgault
G.
,
De Bruille
V.
,
Cao
V.
,
Hauschild
M.
,
Henderson
A.
,
Humbert
S.
,
Kashef-Haghighi
S.
,
Kounina
A.
,
Laurent
A.
,
Levasseur
A.
,
Liard
G.
,
Rosenbaum
R. K.
,
Roy
P.-O.
,
Shaked
S.
,
Fantke
P.
&
Jolliet
O.
2019
IMPACT world + : A globally regionalized life cycle impact assessment method
.
Int. J. Life Cycle Assess.
24
,
1653
1674
.
https://doi.org/10.1007/s11367-019-01583-0
.
Chen
K.-H.
,
Wang
H.-C.
,
Han
J.-L.
,
Liu
W.-Z.
,
Cheng
H.-Y.
,
Liang
B.
&
Wang
A.-J.
2020
The application of footprints for assessing the sustainability of wastewater treatment plants: A review
.
J. Cleaner Prod.
277
,
124053
.
https://doi.org/10.1016/j.jclepro.2020.124053
.
Chen
H.
,
Zheng
Y.
,
Zhou
K.
,
Cheng
R.
,
Zheng
X.
,
Ma
Z.
&
Shi
L.
2023
Carbon emission efficiency evaluation of wastewater treatment plants: Evidence from China
.
Environ. Sci. Pollut. Res.
30
,
76606
76616
.
https://doi.org/10.1007/s11356-023-27685-9
.
Comino
E.
,
Dominici
L.
,
Ambrogio
F.
&
Rosso
M.
2020
Mini-hydro power plant for the improvement of urban water-energy nexus toward sustainability – A case study
.
J. Cleaner Prod.
249
,
119416
.
https://doi.org/10.1016/j.jclepro.2019.119416
.
Corominas
L.
,
Byrne
D. M.
,
Guest
J. S.
,
Hospido
A.
,
Roux
P.
,
Shaw
A.
&
Short
M. D.
2020
The application of life cycle assessment (LCA) to wastewater treatment: A best practice guide and critical review
.
Water Res.
184
,
116058
.
https://doi.org/10.1016/j.watres.2020.116058
.
Daelman
M. R. J.
,
van Voorthuizen
E. M.
,
van Dongen
U. G. J. M.
,
Volcke
E. I. P.
&
van Loosdrecht
M. C. M.
2015
Seasonal and diurnal variability of N2O emissions from a full-scale municipal wastewater treatment plant
.
Sci. Total Environ.
536
,
1
11
.
https://doi.org/10.1016/j.scitotenv.2015.06.122
.
Delanka-Pedige
H. M. K.
,
Munasinghe-Arachchige
S. P.
,
Abeysiriwardana-Arachchige
I. S. A.
&
Nirmalakhandan
N.
2021
Wastewater infrastructure for sustainable cities: Assessment based on UN sustainable development goals (SDGs)
.
Int. J. Sustainable Dev. World Ecol.
28
,
203
209
.
https://doi.org/10.1080/13504509.2020.1795006
.
DOE
2016
Water Quality Standards in Iran
.
Department of Environment
,
Tehran, Iran
.
Franke
N. A.
,
Boyacioglu
H.
&
Hoekstra
A. Y.
2013
Grey Water Footprint Accounting: Tier 1 Supporting Guidelines, Value of Water Research Report Series No. 65, UNESCO-IHE Institute for Water Education
.
Unesco-Ihe Delft
,
The Netherlands
.
Gallego-Schmid
A.
&
Tarpani
R. R. Z.
2019
Life cycle assessment of wastewater treatment in developing countries: A review
.
Water Res.
153
,
63
79
.
https://doi.org/10.1016/j.watres.2019.01.010
.
Goliopoulos
N.
,
Mamais
D.
,
Noutsopoulos
C.
,
Dimopoulou
A.
&
Kounadis
C.
2022
Energy consumption and carbon footprint of Greek wastewater treatment plants
.
Water
14
,
320
.
https://doi.org/10.3390/w14030320
.
Gómez-Llanos
E.
,
Matías-Sánchez
A.
&
Durán-Barroso
P.
2020
Wastewater treatment plant assessment by quantifying the carbon and water footprint
.
Water
12
,
3204
.
https://doi.org/10.3390/w12113204
.
Gu
Y.
,
Dong
Y.
,
Wang
H.
,
Keller
A.
,
Xu
J.
,
Chiramba
T.
&
Li
F.
2016
Quantification of the water, energy and carbon footprints of wastewater treatment plants in China considering a water – energy nexus perspective
.
Ecol. Indic.
60
,
402
409
.
https://doi.org/10.1016/j.ecolind.2015.07.012
.
Gu
Y.
,
Li
Y.
,
Li
X.
,
Luo
P.
,
Wang
H.
,
Wang
X.
,
Wu
J.
&
Li
F.
2017
Energy self-sufficient wastewater treatment plants: Feasibilities and challenges
.
Energy Procedia
105
,
3741
3751
.
https://doi.org/10.1016/j.egypro.2017.03.868
.
Gustavsson
D. J. I.
&
Tumlin
S.
2013
Carbon footprints of Scandinavian wastewater treatment plants
.
887
893
.
https://doi.org/10.2166/wst.2013.318
.
Haitsma Mulier
M. C. G.
,
van de Ven
F. H. M.
&
Kirshen
P.
2022
Circularity in the urban water-energy-nutrients-food nexus
.
Energy Nexus
7
,
100081
.
https://doi.org/10.1016/j.nexus.2022.100081
.
Hauschild
M. Z.
,
Rosenbaum
R. K.
&
Olsen
S. I.
2018
Life Cycle Assessment
.
Springer International Publishing
,
Cham, Switzerland
.
https://doi.org/10.1007/978-3-319-56475-3
.
Hoekstra
A. Y.
,
Chapagain
A. K.
,
Aldaya
M. M.
&
Mekonnen
M. M.
2011
The Water Footprint Assessment Manual: Setting the Global Standard
.
Earthscan, London, UK
.
Huang
F.
,
Shen
W.
,
Zhang
X.
&
Seferlis
P.
2020
Impacts of dissolved oxygen control on different greenhouse gas emission sources in wastewater treatment process
.
J. Cleaner Prod.
274
,
123233
.
https://doi.org/10.1016/j.jclepro.2020.123233
.
Huijbregts
M. A. J.
,
Steinmann
Z. J. N.
,
Elshout
P. M. F.
,
Stam
G.
,
Verones
F.
,
Vieira
M.
,
Zijp
M.
,
Hollander
A.
&
van Zelm
R.
2017
Recipe2016: A harmonised life cycle impact assessment method at midpoint and endpoint level
.
Int. J. Life Cycle Assess.
22
,
138
147
.
https://doi.org/10.1007/s11367-016-1246-y
.
Jamshidi
S.
2019
Value-added innovation in infrastructure systems, lessons learned from wastewater treatment plants
.
TQM J.
31
,
1049
1063
.
https://doi.org/10.1108/TQM-11-2018-0178
.
Jamshidi
S.
&
Naderi
A.
2023b
Wetland restoration policies and the sustainability of agricultural productions, lessons learnt from Zrebar Lake, Iran
. In:
Ecorestoration for Sustainability
.
Wiley
,
Hoboken, NJ, USA
, pp.
113
166
.
https://doi.org/10.1002/9781119879954.ch4
.
Jamshidi
S.
,
Moradkhani
M.
,
Zarei
M.
&
Mamaghani nejad
M.
2023
Performance evaluation and comparison of facultative ponds in series and parallel for wastewater treatment (Case study: Delijan WWTP)
.
J. Civ. Environ. Eng.
53
(
3
),
10
22
.
https://doi.org/10.22034/jcee.2022.52666.2172
.
Kampschreur
M. J.
,
Temmink
H.
,
Kleerebezem
R.
,
Jetten
M. S. M.
&
van Loosdrecht
M. C. M.
2009
Nitrous oxide emission during wastewater treatment
.
Water Res.
43
,
4093
4103
.
https://doi.org/10.1016/j.watres.2009.03.001
.
Karnaningroem
N.
&
Anggraeni
D. R.
2021
Study of Life Cycle Assessment (LCA) on water treatment
.
IOP Conf. Ser. Earth Environ. Sci.
799
,
012036
.
https://doi.org/10.1088/1755-1315/799/1/012036
.
Kurian
M.
,
Scott
C.
,
Reddy
V. R.
,
Alabaster
G.
,
Nardocci
A.
,
Portney
K.
,
Boer
R.
&
Hannibal
B.
2019
One swallow does not make a summer: siloes, Trade-Offs and Synergies in the Water-Energy-Food Nexus
.
Front. Environ. Sci.
7
.
https://doi.org/10.3389/fenvs.2019.00032
.
Madani
K.
,
AghaKouchak
A.
&
Mirchi
A.
2016
Iran's socio-economic drought: Challenges of a water-Bankrupt nation
.
Iran. Stud.
49
,
997
1016
.
https://doi.org/10.1080/00210862.2016.1259286
.
Maktabifard
M.
,
Zaborowska
E.
&
Makinia
J.
2019
Evaluating the effect of different operational strategies on the carbon footprint of wastewater treatment plants – case studies from northern Poland
.
Water Sci. Technol.
79
,
2211
2220
.
https://doi.org/10.2166/wst.2019.224
.
Maktabifard
M.
,
Zaborowska
E.
&
Makinia
J.
2020
Energy neutrality versus carbon footprint minimization in municipal wastewater treatment plants
.
Bioresour. Technol.
300
,
122647
.
https://doi.org/10.1016/j.biortech.2019.122647
.
Malik
O. A.
,
Hsu
A.
,
Johnson
L. A.
&
de Sherbinin
A.
2015
A global indicator of wastewater treatment to inform the Sustainable Development Goals (SDGs)
.
Environ. Sci. Policy
48
,
172
185
.
https://doi.org/10.1016/j.envsci.2015.01.005
.
Mamais
D.
,
Noutsopoulos
C.
,
Dimopoulou
A.
,
Stasinakis
A.
&
Lekkas
T. D.
2015
Wastewater treatment process impact on energy savings and greenhouse gas emissions
.
Water Sci. Technol.
71
,
303
308
.
https://doi.org/10.2166/wst.2014.521
.
Marinelli
E.
,
Radini
S.
,
Akyol
Ç.
,
Sgroi
M.
,
Eusebi
A. L.
,
Bischetti
G. B.
,
Mancini
A.
&
Fatone
F.
2021
Water-Energy-Food-Climate nexus in an integrated peri-Urban wastewater treatment and reuse system: from theory to practice
.
Sustainability
13
,
10952
.
https://doi.org/10.3390/su131910952
.
Miller-Robbie
L.
,
Ramaswami
A.
&
Amerasinghe
P.
2017
Wastewater treatment and reuse in urban agriculture: Exploring the food, energy, water, and health nexus in Hyderabad, India
.
Environ. Res. Lett.
12
,
075005
.
https://doi.org/10.1088/1748-9326/aa6bfe
.
Mo
W.
&
Zhang
Q.
2013
Energy–nutrients–water nexus: Integrated resource recovery in municipal wastewater treatment plants
.
J. Environ. Manage.
127
,
255
267
.
https://doi.org/10.1016/j.jenvman.2013.05.007
.
Morera
S.
,
Corominas
L.
,
Poch
M.
,
Aldaya
M. M.
&
Comas
J.
2016
Water footprint assessment in wastewater treatment plants
.
J. Cleaner Prod.
112
,
4741
4748
.
https://doi.org/10.1016/j.jclepro.2015.05.102
.
Mulchandani
A.
&
Westerhoff
P.
2016
Recovery opportunities for metals and energy from sewage sludges
.
Bioresour. Technol.
215
,
215
226
.
https://doi.org/10.1016/j.biortech.2016.03.075
.
NWWC
2021
Annual report of Iranian national water and wastewater engineering company
.
Shahrab
24
(
699
),
1
64
.
Obaideen
K.
,
Shehata
N.
,
Sayed
E. T.
,
Abdelkareem
M. A.
,
Mahmoud
M. S.
&
Olabi
A. G.
2022
The role of wastewater treatment in achieving sustainable development goals (SDGs) and sustainability guideline
.
Energy Nexus
7
,
100112
.
https://doi.org/10.1016/j.nexus.2022.100112
.
Pacetti
T.
,
Lombardi
L.
&
Federici
G.
2015
Water–energy nexus: A case of biogas production from energy crops evaluated by water footprint and Life Cycle Assessment (LCA) methods
.
J. Cleaner Prod.
101
,
278
291
.
https://doi.org/10.1016/j.jclepro.2015.03.084
.
Parravicini
V.
,
Svardal
K.
&
Krampe
J.
2016
Greenhouse Gas emissions from wastewater treatment plants
.
Energy Procedia
97
,
246
253
.
https://doi.org/10.1016/j.egypro.2016.10.067
.
Ramachandra
T. V.
&
Mahapatra
D. M.
2015
The science of carbon footprint assessment
.
Carbon Footprint Handbook
3
44
.
https://doi.org/10.1201/b18929-3
.
Ramirez
C.
,
Almulla
Y.
&
Fuso Nerini
F.
2021
Reusing wastewater for agricultural irrigation: A water-energy-food nexus assessment in the North Western Sahara aquifer system
.
Environ. Res. Lett.
16
,
044052
.
https://doi.org/10.1088/1748-9326/abe780
.
Riccardo
B.
,
Paolo
V.
,
Davide
L.
,
Nicoletta
S.
,
Vincenzo
T.
,
Marco
R.
,
Gabriela
I.
&
Cristina
R. E.
2023
A study on the carbon footprint contributions from a large wastewater treatment plant
.
Energy Rep.
9
,
274
286
.
https://doi.org/10.1016/j.egyr.2023.06.002
.
Rosenbaum
R. K.
,
Hauschild
M. Z.
,
Boulay
A. N.
,
Fantke
P.
,
Laurent
A.
,
Núñez
M.
,
Vieira
M.
,
2018
Life cycle impact assessment
. In:
Life Cycle Assessment, Theory and Practice
(
Hauschild
M. Z.
,
Rosenbaum
R. K.
&
Olsen
S. I.
, eds).
Springer
,
Cham, Switzerland
, pp.
167
270
.
Roudbari
M. V.
,
Dehnavi
A.
,
Jamshidi
S.
&
Yazdani
M.
2023
A multi-pollutant pilot study to evaluate the grey water footprint of irrigated paddy rice
.
Agric. Water Manage.
282
,
108291
.
https://doi.org/10.1016/j.agwat.2023.108291
.
Sabeen
A. H.
,
Noor
Z. Z.
,
Ngadi
N.
,
Almuraisy
S.
&
Raheem
A. B.
2018
Quantification of environmental impacts of domestic wastewater treatment using life cycle assessment: A review
.
J. Cleaner Prod.
190
,
221
233
.
https://doi.org/10.1016/j.jclepro.2018.04.053
.
Schindler
D. W.
,
Carpenter
S. R.
,
Chapra
S. C.
,
Hecky
R. E.
&
Orihel
D. M.
2016
Reducing phosphorus to curb lake eutrophication is a success
.
Environ. Sci. Technol.
50
,
8923
8929
.
Singh
P.
,
Kansal
A.
&
Carliell-Marquet
C.
2016
Energy and carbon footprints of sewage treatment methods
.
J. Environ. Manage.
165
,
22
30
.
https://doi.org/10.1016/j.jenvman.2015.09.017
.
Suryawan
I. W. K.
,
Rahman
A.
,
Lim
J.-W.
&
Helmy
Q.
2021
Environmental impact of municipal wastewater management based on analysis of life cycle assessment in Denpasar City
.
Desalin. Water Treat.
244
,
55
62
.
https://doi.org/10.5004/dwt.2021.27957
.
Tahar
A.
,
Kennedy
A. M.
,
Fitzgerald
R. D.
,
Clifford
E.
&
Rowan
N.
2018
Longitudinal evaluation of the impact of traditional rainbow trout farming on receiving water quality in Ireland
.
PeerJ
2018
,
1
22
.
https://doi.org/10.7717/peerj.5281
.
Tamburini
E.
,
Fano
E. A.
,
Castaldelli
G.
&
Turolla
E.
2019
Life cycle assessment of oyster farming in the po delta, Northern Italy
.
Resources
8
,
1
17
.
https://doi.org/10.3390/resources8040170
.
Tayyebi
F.
,
Golabi
M.
&
Jaafarzadeh
N.
2023
Life cycle assessment, a decision-making tool in wastewater treatment systems: A case study wastewater treatment plant of Ahvaz, Iran
.
Appl. Water Sci.
13
,
145
.
https://doi.org/10.1007/s13201-023-01958-7
.
Tong
T.
&
Elimelech
M.
2016
The global rise of zero liquid discharge for wastewater management: drivers, technologies, and future directions
.
Environ. Sci. Technol.
50
,
6846
6855
.
https://doi.org/10.1021/acs.est.6b01000
.
Wendling
Z. A.
,
Emerson
J. W.
,
Esty
D. C.
,
Levy
M. A.
&
de Sherbinin
A.
2018
Environmental Performance Index 2018
.
Yale University, New Haven, CT, USA
, p.
123
.
Wilson
L.
,
Lichinga
K. N.
,
Kilindu
A. B.
&
Masse
A. A.
2021
Water utilities’ improvement: The need for water and energy management techniques and skills
.
Water Cycle
2
,
32
37
.
https://doi.org/10.1016/j.watcyc.2021.05.002
.
WRI
2020
Master Plan and Roadmap of Wastewater Treatment (in Persian)
.
Water Research Institute
,
Tehran, Iran
.
Wu
Z.
,
Duan
H.
,
Li
K.
&
Ye
L.
2022
A comprehensive carbon footprint analysis of different wastewater treatment plant configurations
.
Environ. Res.
214
,
113818
.
https://doi.org/10.1016/j.envres.2022.113818
.
Yazdian
H.
&
Jamshidi
S.
2021
Performance evaluation of wastewater treatment plants under the sewage variations imposed by COVID-19 spread prevention actions
.
J. Environ. Heal. Sci. Eng.
.
https://doi.org/10.1007/s40201-021-00717-7
.
Yoshida
H.
,
Clavreul
J.
,
Scheutz
C.
&
Christensen
T. H.
2014
Influence of data collection schemes on the life cycle assessment of a municipal wastewater treatment plant
.
Water Res.
56
,
292
303
.
https://doi.org/10.1016/j.watres.2014.03.014
.
Yoshida
H.
,
ten Hoeve
M.
,
Christensen
T. H.
,
Bruun
S.
,
Jensen
L. S.
&
Scheutz
C.
2018
Life cycle assessment of sewage sludge management options including long-term impacts after land application
.
J. Cleaner Prod.
174
,
538
547
.
https://doi.org/10.1016/j.jclepro.2017.10.175
.
Zawartka
P.
,
Burchart-Korol
D.
&
Blaut
A.
2020
Model of carbon footprint assessment for the life cycle of the system of wastewater collection, transport and treatment
.
Sci. Rep.
10
,
5799
.
https://doi.org/10.1038/s41598-020-62798-y
.
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