A Standardized Stoichiometric Life-Cycle Inventory for Enhanced Specificity in Environmental Assessment of Sewage Treatment

Abstract

In recent years, many life-cycle assessments (LCAs) have been applied to the field of sewage treatment (ST). However, most LCAs lack systematic data collection (DC) and processing methods for inventories of conventional ST (CST), much less for recently developed technologies. In addition, the use of site-generic databases results in LCAs that lack the representativeness and understanding of the regional environmental impacts and trade-offs between different impact categories, especially nutrient enrichment and toxicity-related categories. These shortcomings make comparative evaluation and implementation more challenging. In order to assist in the decision-making process, a novel stoichiometric life-cycle inventory (S-LCI) for ST was developed. In the S-LCI, biochemical pathways derived from elemental analyses combined with process-engineering calculations enable steady-state comparison of the water, air, and soil emissions of any sewage and sludge sample treated through the ST configurations analyzed herein. The DC required for the estimation of the foreground data for a CST is summarized in a 41-item checklist. Moreover, the S-LCI was validated for CST by comparing the S-LCI with actual ST plant operations performed in Hong Kong. A novel energy-derived ST inventory is developed and compared here with the CST. The resulting inventories are ready to be integrated into the SimaPro software for life cycle impact assessment as illustrated by the case study. Using the S-LCI not only helps to standardize the DC and processing, but it also enhances the level of specificity by using sample characterization and site-specific data. The EcoInvent database, which contains a single sample characterization per Swiss and global average ST plant class could be expanded by using the S-LCI.

Beyond Energy Balance: Environmental Trade-Offs of Organics Capture and Low Carbon-to-Nitrogen Ratio Sewage Treatment Systems

“…In this study, the foreground information follows the S-LCI methodology for enhanced standardization and specificity. 41 The S-LCI consists of three main steps: data collection, calculations, and results. In the S-LCI, the results of the elemental analysis of the samples (Figure 1; data flow 2) help in constructing the specific empirical formulas for microbial cells.…”

“…A standardized stoichiometric life-cycle inventory (S-LCI) framework combines biochemistry and process engineering to obtain steady-state and site-specific air, water, and soil emissions for a WWT. 41 scenarios are analyzed through LCA. Scenario 1 (S1) represents a conventional system with activated sludge.…”

“…The LCIs of S1 and S3 have been presented elsewhere using the S-LCI framework. 41 However, the S-LCI results for S1 and S3 need further analysis through impact assessment methodologies (IAMs) and interpretation methods to obtain a clearer trend of their environmental impacts. This paper has a 3fold objective: (i) to compare the environmental trade-offs of CEPT to those of conventional primary treatment, (ii) to determine environmental hotspots in different plant-wide configurations for low-strength wastewater in a subtropical city, and (iii) to demonstrate the integration of the S-LCI framework into LCA.…”

Residuals, sludge, and biosolids: Advancements in the field

“…There have been several overview articles related to tracing the overall life cycle of sludge and biosolids from cradle to grave to examine the potential impacts and opportunities. Alvarado et al (2019) utilized a standardized stoichiometric life cycle inventory to aid in decision making and attempt to address regional impacts and categorical differences in analysis. The team presented a validated inventory based on biochemical pathways, process engineering, and air/soil emissions as a resource that may be helpful for wastewater recovery facilities (WWRF).…”

Improving the reliability of chemical manufacturing life cycle inventory constructed using secondary data

“…This work is interesting because the authors developed methods to model ancillary energy processes (Jiménez‐González, 2000b) separate from the chemical of interest (Jiménez‐González et al., 2000a). Numerous other examples of using process design in LCI modeling have followed (Alvarado et al., 2019; Geisler, Hofstetter, & Hungerbühler, 2004; Parvatker et al., 2019; Simon et al., 2019; Yao, 2018), with this approach eventually expanding to include full process simulation (Liao, Kelley, & Yao, 2020; Smith et al., 2017). Although effective, process design and simulation often require detailed process knowledge and chemical engineering expertise that may not be practical or readily available (Meyer et al., 2019; Parvatker, 2018).…”