Modeling the oxygen-depleting potential and spatially differentiated effect of sewage organics in life cycle assessment for wastewater management

Prospective Life Cycle Assessment for the Electrochemical Oxidation Wastewater Treatment Process: From Laboratory to Industrial Scale

Electrochemical oxidation (EO) is a promising technology for water purification, but indirect environmental burdens may arise in association with consumption of materials and energy during electrode preparation and process operation. This study evaluated the life cycle environmental impacts of emerging EO technology from laboratory scale to industrial scale using prospective life cycle assessment (LCA) on a quantitative basis. Environmental impacts of EO technology were assessed at laboratory scale by comparing three representative anode materials (SnO2, PbO2, and boron-doped diamond) and other two typical processes (adsorption and Fenton method), which verified the competitiveness of the EO process and identified the key factors to environmental hotspots. Thereafter, LCA of scale-up EO was performed to offer guidance for practical application, and the life cycle inventory was compiled upon thermodynamic and kinetic simulations, empirical calculation rules, and similar technical information. Results demonstrated EO to be effective for destructing recalcitrant organic pollutants, but visible direct benefits might be outweighed by increased indirect environmental burdens associated with the preparation of anode materials, use of electrolytes, and energy consumption during the operation stage at both laboratory scale and larger scale. This necessitated attention to overall life cycle profiles by taking into account reactor design, anode materials, electrolyte and flow pattern, and decentralized location with a large share of renewable power station and rigorous contamination control strategies for wastewater treatment plants.

Combining the multivariate statistics and dual stable isotopes methods for nitrogen source identification in coastal rivers of Hangzhou Bay, China

Coastal rivers contributed the majority of anthropogenic nitrogen (N) loads to coastal waters, often resulting in eutrophication and hypoxia zones. Accurate N source identification is critical for optimizing coastal river N pollution control strategies. Based on a 2-year seasonal record of dual stable isotopes ([Formula: see text] and [Formula: see text]) and water quality parameters, this study combined the dual stable isotope-based MixSIAR model and the absolute principal component score-multiple linear regression (APCS-MLR) model to elucidate N dynamics and sources in two coastal rivers of Hangzhou Bay. Water quality/trophic level indices indicated light-to-moderate eutrophication status for the studied rivers. Spatio-temporal variability of water quality was associated with seasonal agricultural, aquaculture, and domestic activities, as well as the seasonal precipitation pattern. The APCS-MLR model identified soil + domestic wastewater (69.5%) and aquaculture tailwater (22.2%) as the major nitrogen pollution sources. The dual stable isotope-based MixSIAR model identified soil N, aquaculture tailwater, domestic wastewater, and atmospheric deposition N contributions of 35.3 ±21.1%, 29.7 ±17.2%, 27.9 ±14.5%, and 7.2 ±11.4% to riverine [Formula: see text] in the Cao'e River (CER) and 34.4 ±21.3%, 29.5 ±17.2%, 27.4 ±14.7%, and 8.7 ±12.8% in the Jiantang River (JTR), respectively. The APCS-MLR model and the dual stable isotope-based MixSIAR model showed consistent results for riverine N source identification. Combining these two methods for riverine N source identifications effectively distinguished the mix-source components from the APCS-MLR method and alleviated the high cost of stable isotope analysis, thereby providing reliable N source apportionment results with low requirements for water quality sampling and isotope analysis costs. This study highlights the importance of soil N management and aquaculture tailwater treatment in coastal river N pollution control.

Establishing a decision-support system for eco-design of biological wastewater treatment: A case study of bioaugmented constructed wetland

Deep treatment is a common approach to enhance pollutant removal for biological wastewater treatment technologies (BWTTs), and life cycle assessment (LCA) holds substantial advantages to support process optimization. However, there lacks of LCA-based benchmarks that cover human-nature nexuses and stakeholder involvement, which limits the guidance and eco-design of BWTTs. This study proposed a decision-support system (DSS) by linking LCA with Water Quality Model and Conjoint Analysis. Three major findings were identified based on a demonstrative case (constructed wetland bioaugmented by dosing different microbial inocula): (1) Increasing bacterial intensities would achieve net environmental improvement, but it might not apply to all cases; (2) Making full use of natural self-purification capacity could partly replace the functions of BWTTs; (3) Stakeholders would concern aquatic environmental improvement when receiving river that had limited environmental capacity. Overall, the DSS provided a data-driven platform for screening options before determinations were made to constrain wastewater treatment sustainability.