ABSTRACT
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.
HIGHLIGHTS
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.
INTRODUCTION
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.
METHODS
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).
The specifications of studied WWTPs
WWTPs . | ID . | Process . | Population . | Flow (m3/d) . | Climate . | Reuse 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 |
WWTPs . | ID . | Process . | Population . | Flow (m3/d) . | Climate . | Reuse 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 |
Location and spatial distribution of studied WWTPs in different climates.
Water footprint
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.
Assumed Cmax and Cnat for GWF accounting in two scenarios
Parameters . | Cmax (mg/L) . | Cnat (mg/L) . | Reference . | |
---|---|---|---|---|
Discharge . | Reuse . | |||
Total suspended solids (TSS) | 50 | 100 | 10 | DOE (2016) |
Chemical oxidation demand (COD) | 20 | 60 | 5 | |
Biochemical oxidation demand (BOD) | 10 | 30 | 2 |
Parameters . | Cmax (mg/L) . | Cnat (mg/L) . | Reference . | |
---|---|---|---|---|
Discharge . | Reuse . | |||
Total suspended solids (TSS) | 50 | 100 | 10 | DOE (2016) |
Chemical oxidation demand (COD) | 20 | 60 | 5 | |
Biochemical oxidation demand (BOD) | 10 | 30 | 2 |
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.
Impact categories and the conversion coefficients of ReCiPe (Huijbregts et al. 2017)
Impact category . | Midpoint . | Endpoint . | Normalization factor (N) . | |||
---|---|---|---|---|---|---|
Influencing factor . | Coefficient (M) . | Unit . | Coefficient (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 category . | Midpoint . | Endpoint . | Normalization factor (N) . | |||
---|---|---|---|---|---|---|
Influencing factor . | Coefficient (M) . | Unit . | Coefficient (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.
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).
RESULTS AND DISCUSSION
Water footprint
Pollution removal (%) of WWTPs based on treatment units (a) and climates (b).
The grey water footprint of WWTPs’ effluent based on main pollutants (a) and with wastewater reuse (b).
The grey water footprint of WWTPs’ effluent based on main pollutants (a) and with wastewater reuse (b).
The GWF and BWF of WWTPs and their ratios with and without wastewater reuse.
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
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.
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.
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).
The impacts of wastewater treatment and reuse on the cumulative environmental damages (CI).
The impacts of wastewater treatment and reuse on the cumulative environmental damages (CI).
Overall performance of WWTPs based on their units, reuse, and sizes. More CI removal shows higher performance by the developed method.
Overall performance of WWTPs based on their units, reuse, and sizes. More CI removal shows higher performance by the developed method.
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.
CONCLUSIONS
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.
ACKNOWLEDGEMENTS
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.
AUTHOR CONTRIBUTIONS
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.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
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