1. Introduction

2. Methodology

2.1. Life Cycle Systainability Assessment

2.1.1. Study Sites

The two WW-CEs in Santiago, Chile referred to as Plant A and Plant B, currently serve total population equivalents (p.e., including domestic and industrial wastewater) of 6,045,292 and 4,188,539; and influent flowrates of 8.6 and 7.2 m³/ s, respectively. Both Plants are property of the LWC, that manage all potable water and wastewater plants of the MR. Each Plant was developed as a response to requirements for improved sanitation coverage and waste management in the MR. They are located at different locations along the Mapocho River, the main river running through the MR catchment, affecting different local communities. The Plants transitioned from conventional WWTPs to Biofactory WW-CE configurations through the recovery of waste streams to value added resources. Specifically, and in different capacities, treated effluent, biogas, nutrients and biosolids were recovered as products via alternative technology configurations and stakeholder engagement in each Plant. Biosolids produced by the plants are managed by the same biosolids recovery system. Additionally, some of the same chemical suppliers serve both plants.

2.1.2. Goal and Scope

The goal of the study was to quantify integral life cycle sustainability impacts, combining LCA, LCC and SLCA, of the two Plants. The aim was to verify if the implementation of a WW-CE improves integral sustainability overall in the context of Chile. Additionally, the Plants were compared to determine which Biofactory WW-CE configuration best improved integral sustainability impacts. The integral LSCA methodology was established by the ISO LCA guidelines that establish complementary SLCA and LCC methodologies also [23,24,25]. The life cycle of the Plants was estimated at 20-50 years for equipment and over 100 years for civil works, resulting in low relative contribution to environmental and socio-cultural impacts, therefore, this study considered the operation stage only [31]. Daily Plant impacts were averaged over one year of operation, for the treatment of a 1,000,000 p.e. as the adopted functional unit (FU). This considered 44.5 g Biological Oxygen Demand (BOD₅)/ person/ day, resulting in a reference flow of 44,500 kg BOD₅/ day treated in wastewater influent. Treated effluent, biogas, biosolids and return flows established corresponding waste and product reference flows for the resource recovery and advanced treatment scenarios established within the system boundaries. Environmental, social, and economic burdens were modelled by grouping unit processes by product systems, considering either mass (kg) or energy (kWh) of products. This was facilitated by collaboration with the LWC providing operational data and expert interviews.

2.1.3. System Boundaries

The scenarios analysed presented gate-to-cradle transition to gate-to-gate. The system boundaries from an integral sustainability perspective included identification of both technologies and stakeholders. Figure 1 and Figure 2 show the system boundaries of Plant A and Plant B respectively. The boundary began with the reception of wastewater post preliminary treatment removing larger contaminants, considered negligible. Plant unit processes were grouped by product and co-product systems. SLCA system boundaries determined the inclusion of Workers (W), value chain actors (VCA), clients (C), local community and children’s concerns (LC), wider society (WS) and farmers (F) as stakeholders. F were included as a separate stakeholder considering unique social impacts related to the Plants role in agriculture as a Biofactory WW-CE. WS refers to relationships with regulatory bodies and national policies related to sustainability.

A 0^th scenario (S0) was the direct discharge of wastewater to the environment without treatment, modelled with influent wastewater data for LCA and set to all 0 values for SLCA and LCC. Scenario 1 (S1): Conventional WWTPs established a baseline system of WWTP with no product recovery, delineated by the solid black line in Figure 1. The technologies considered were primary sedimentation, aerobic reactors, secondary clarification (waste activated sludge WAS) and disinfection then discharge in both Plants. Likewise, sludge treatment involved primary and secondary sludge thickening, anaerobic digestion (AD), sludge dewatering via centrifuge with 100% of biogas flared and biosolids sent to landfill. The design of treatment technologies in each Plant under this scenario varied and more information is available in Table S1. Stakeholders included on site W, VCA for chemical and service supplies, LC affected by the Plants in general and WS.

Scenario 2 (S2): WW-CE considered current configurations of respective Plants as Biofactory WW-CEs, expanding system boundaries (dashed line) to include partial water recovery, biosolids recovery to agriculture, biogas recovery for energy generation and advanced nitrogen removal systems. Plant A provided local F with irrigation water for ‘fertigation’ via effluent raceway, considering avoided water consumption credits (20 % of discharged effluent volume). Plant B did not recover treated effluent, however, implemented additional sludge pre-thickening and THP processes, improving biogas production and biosolids quality (Devos et al., 2021). Different ratios of biosolids management to landfill and agricultural applications (75 % application in Plant A and 87 % application in Plant B) reflect biosolids quality that must comply as class B, where assumed avoided fertilizer consumption provided environmental credit (BCN, 2009). W, LC, VCA and WS of biosolids management were incorporated, as well F benefit by biosolids recovery. Transport of biosolids to landfill and agriculture pastures was considered, excluding the impact of heavy vehicles and machinery used for the application of biosolids to agricultural crops and crop production, however, improved yield was considered as socio-cultural benefit. Biogas energy recovery involved H₂S removal by chemical absorption scrubbing and biological precipitation, for both Plants. CO₂ removal by pressurized membrane separation produced domestic biomethane supply (DBM) in Plant A and avoided domestic natural gas consumption credits. Plant B used CHP to produce electricity, 12 % was injected to the grid and 88 % used within the plant for self-sufficiency, resulting in overall 80% avoided network energy consumption across the plant. 12% of the CHP heat provided vapor to THP process for additional avoided network energy consumption. For both systems, W, LC, VCA, C and WS were incorporated. Nitrogen removal technologies were implemented in sludge centrifuge return flows, initially directly recycled to primary treatment. Plant A required <1,000 mg/ L Total Solids (TS) for the ‘Demon’ anammox treatment, via coagulation-flocculation ‘Densadeg’ technology and a sequencing batch reactor (SBR) for nitrifier activation. Plant A had higher N in influent and effluent, requiring higher aeration flowrates and higher Cl₂ dosage for nitrification in the water line. For Plant B, SBR reactors with high aeration rates were implemented for nitrification and solids removal, followed by ‘Demon’ anammox systems. W, VCA and WS were identified stakeholders involved in this system.

Figure 1. Plant A system boundaries for scenario 0: wastewater discharge without treatment; scenario 1: conventional wastewater treatment plant (WWTP); and scenario 2: biofactory wastewater circular economy (WW-CE). Abbreviations: PS, primary sedimentation, AR, aerobic reactor, SC, secondary clarifier, PST, primary sludge thickening, SST, secondary sludge thickening, AD, anerobic digestor, DBM, domestic biomethane, SBR, sequencing batch reactor. Legend: blue box: effluent discharge; orange box: sludge treatment and biosolids disposal to landfill; yellow box: biogas flare; light blue box: partial water recovery; dark blue box: biosolids recovery to agriculture; light green box: biogas energy recovery; dark green box: advanced nitrogen removal.

Figure 1. Plant A system boundaries for scenario 0: wastewater discharge without treatment; scenario 1: conventional wastewater treatment plant (WWTP); and scenario 2: biofactory wastewater circular economy (WW-CE). Abbreviations: PS, primary sedimentation, AR, aerobic reactor, SC, secondary clarifier, PST, primary sludge thickening, SST, secondary sludge thickening, AD, anerobic digestor, DBM, domestic biomethane, SBR, sequencing batch reactor. Legend: blue box: effluent discharge; orange box: sludge treatment and biosolids disposal to landfill; yellow box: biogas flare; light blue box: partial water recovery; dark blue box: biosolids recovery to agriculture; light green box: biogas energy recovery; dark green box: advanced nitrogen removal.

Figure 2. Plant B system boundaries for scenario 0: wastewater discharge without treatment; scenario 1: conventional wastewater treatment plant (WWTP); and scenario 2: biofactory wastewater circular economy (WW-CE). Abbreviations: PS, primary sedimentation; AR, aerobic reactor; SC, secondary clarifier; PST, primary sludge thickening; SST, secondary sludge thickening; AD, anerobic digestor; P-SST, pre-secondary sludge thickening; THP, thermal hydrolysis pre-treatment; CHP, cogeneration heath and power; SBR, sequencing batch reactor. Legend: blue box: effluent discharge, orange box: sludge treatment and biosolids disposal to landfill; yellow box: biogas flare; dark blue box: biosolids recovery to agriculture; light green box: biogas energy recovery; dark green box: advanced nitrogen removal.

Figure 2. Plant B system boundaries for scenario 0: wastewater discharge without treatment; scenario 1: conventional wastewater treatment plant (WWTP); and scenario 2: biofactory wastewater circular economy (WW-CE). Abbreviations: PS, primary sedimentation; AR, aerobic reactor; SC, secondary clarifier; PST, primary sludge thickening; SST, secondary sludge thickening; AD, anerobic digestor; P-SST, pre-secondary sludge thickening; THP, thermal hydrolysis pre-treatment; CHP, cogeneration heath and power; SBR, sequencing batch reactor. Legend: blue box: effluent discharge, orange box: sludge treatment and biosolids disposal to landfill; yellow box: biogas flare; dark blue box: biosolids recovery to agriculture; light green box: biogas energy recovery; dark green box: advanced nitrogen removal.

2.1.4. Integrated Life Cycle Inventories

For LCA inventories, material flow analysis (MFA) was conducted of wastewater across all unit processes defined in Figure 1 and Figure 2, determining influent, effluent, return and biosolids flows. MFA substances were TS, Volatile Solids (VS), Total Nitrogen (TN) and Phosphorous (TP), BOD₅, chemical oxygen demand (COD) and 14 heavy meals (Table S2 and S3). Chemical consumption, energy consumption, transport processes, atmospheric GHG emissions, products and avoided products were also included in the LCA inventory (Table S5 and S6). LCC integrated capital investment, maintenance costs, operational costs and income with LCA [32]. Data was compiled from operational data, internal reports, and literature sources (Table S4 and S5). Socio-cultural impacts were quantified by SLCA Type II midpoint characterization of social indicators, measuring relationship between stakeholders with ‘activity variables’ (i.e. training hours) according to recommendations from methodological guidelines for stakeholder impact assessment [33]. The quantity of stakeholders involved in each product system were identified by expert W (internal stakeholders) within the LWC who interact with external stakeholders (VCA, C, LC, SC, F) (Table S7). The expert W were interviewed regarding their relationships with external stakeholders, quantifying respective activity variables. This approach was taken due to the sensitivity of external researchers’ intervention with LWCs established relationships with stakeholders. SLCA data was verified by field observations, supporting documents and relevant national legislation [34]. Historic documents quantified some indicators in conventional WWTPs (S1) where expert W interviewees were unable to quantify these indicators (Table S8). A generalized structure of the integrated LCSA data inventories is presented in Figure 3.

2.1.3. Impact Characterization

For LCA, ecosphere and technosphere flows were modelled using the EcoInvent databases. The impact characterization method was ReCiPe Midpoint (H) (world/ 2.0), including 17 impact indicators, determining the environmental decision criteria. The Type II SLCA methodology included 13 impact indicators quantified as activity variable across affected stakeholders and for each product system; and summed by scenario. For the Type II methodology, indicators were assigned monetized factors to quantify sub-category impacts (Table S9). For LCC, the Net Present Value (NPV) was calculated based on the cost inventory according to equation 1:

$$NPV\left(\$\right)={\sum }_{t=1}^1\frac{I-C_{OpEx}-C_{Main}}{{(1+r)}^t}-C_{CapEx}$$

(1)

Where $r$ was the discount rate, set to 8 % according to LWC, $t$ was time period of future cash flow set to 1 year, $I$ was income, $C_{OpEx}$ was operation costs, $C_{CapEx}$ and $C_{Main}$ were initial capital investment and average annual maintenance cost of the respective product systems normalized by Plant life cycles and reference flows. LCA and LCC cash flows were characterized using SimaPro, where Type II SLCA and NPV indicators were calculated externally. Figure 3 presents the summary of LCSA impact indicators as a decision criterion.

2.2. Interpretation with Multi-ctieria Assessment of Sustainability Impacts

2.2.1. Overview of Decision Criteria

MCDM involves establishing alternatives for comparison, performance criteria, criteria weights, and applying a ranking method [18]. Six total alternatives $i$ were considered from the three scenarios comparing both Plants. The decision criteria involved three levels, indicator criteria ($v_{i,j}$), sub-category criteria ($C_{i,j}$) and sustainability criteria ($S_{i,j}$), to determine the overall sustainability score ($T_i)$ according to the decision tree in Figure 3. The decision tree was organized as such to facilitate subjective weighting methodologies and to break down the decision problem into domains that were easier for decision makers to interpret [35]. Environmental impact results generated in SimaPro were considered non-beneficial criteria, indicators positive values represented environmental impacts, therefore, impact categories results were normalized linearly according to:

$$\overline{v_{i,j}}=\frac{v_{i,j}-\mathrm{m}\mathrm{i}\mathrm{n}\left(v_{i-n}\right)}{\max \left(v_{i-n}\right)-\min \left(v_{i-n}\right)}$$

(2)

Where $i$ is the number of alternatives $n$. Social and economic indicators were considered beneficial criteria, where positive values represented social and economic benefits, therefore, indicator results were normalized linearly according to:

$$\overline{v_{i,j}}=\frac{v_{i,j}-\mathrm{m}\mathrm{a}\mathrm{x}\left(v_{i-n}\right)}{\min \left(v_{i-n}\right)-\max \left(v_{i-n}\right)}$$

(3)

Figure 4. Life Cycle Sustainability Assessment decision tree indicator criteria, measurement units, with respective abbreviations, sub-category criteria and sustainability criteria.

Figure 4. Life Cycle Sustainability Assessment decision tree indicator criteria, measurement units, with respective abbreviations, sub-category criteria and sustainability criteria.

2.2.2. Criteria Weighting

A subjective weighting process was implemented to communicate results using Ranked Reciprocal Weighting method (RRW). The use of AHP resulted in an exhaustive quantity of pairwise comparisons for decision makers in this context. RRW produces similar weighting factors when compared to AHP [36]. A survey was designed to rank the relative LCSA indicators, sub-categories, and sustainability criteria in terms of the relative importance (Supplementary Data 1). A panel of experts involved in decision making processes from the LWC responded to the survey ranking factors according to their perspective of the most important protection areas related to the WW-CE implemented in the respective Biofactories. Average ranking, $r_j$, positions of criteria $j$ were calculated, where $m$ is the total number of indicators per sub-category, sub-category per sustainability criteria and overall sustainability criteria. The following equation was applied to calculate the weighting factors:

$$w_j=\frac{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$r_j$}\right.}{\sum _{j=1}^m\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$r_m$}\right.\right)}$$

(4)

2.2.3 Aggregated Sustainability Scores

A wide variety of aggregation measures can be implemented for determining performance of alternatives with respect to decision criteria [18]. In this case, Multi-attribute value theory (MAVT) was selected due to common use in wastewater treatment decision problems, with discrete performance criteria, and uncertainty regarding criteria weights is unknown. [23,24,25]. The weighted sum method was applied according to:

$$C_{i,j}=\sum _{j=1}^m{w}_{I,j}\overline{v_{i,j}}$$

(5)

$$S_{i,j}=\sum _{j=1}^m{w}_{C,j}{C}_{i,j}$$

(6)

$$T_i=\sum _{j=1}^m{w}_{S,j}{S}_{i,j}$$

(7)

Where, $w_{I,j}$, $w_{C,j}$ and $w_{S,j}$ are the indicator, sub-category, and sustainability weighting factors respectively. Weighted sum performance matrices were calculated for the total number of criteria in sub-category $C_{i,j}$ and sustainability criteria $S_{i,j}$ to produce a sustainability score $T_i$ of alternative scenarios $i$, the lowest score demonstrated improved sustainability according to the normalization equations.

3. Results and Discussion

4. Discussion

5. Conclusions

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