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

Valeria I. Alvarado, Shu Chien Hsu, Zhuoying Wu, Chor Man Lam, Ling Leng, Huichuan Zhuang, Po Heng Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5111-5123
Number of pages13
JournalEnvironmental Science and Technology
Volume53
Issue number9
DOIs
Publication statusPublished - 7 May 2019

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry

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