Life Cycle Assessment of Microalgae-Based Domestic Wastewater Treatment and Biochar Production for Enhanced Sustainability
Bibliographic record
Abstract
While several studies have examined phycoremediation to treat domestic wastewater (DWW), a research gap persists on the environmental impacts associated with the resultant algal biomass.Hence, this study aims to assess the implications of microalgae-based systems for domestic wastewater treatment and biochar production for further applications using the Life Cycle Assessment (LCA) approach.Particularly, two systems were investigated: 1) an algalbased system treating DWW, and 2) another system for biochar production from microalgae grown in the selected wastewater.LCA boundary involved inputs (energy, DWW, and chemicals), and outputs (treated effluent, algal biomass, and biochar).Results showed that the scenario producing biochar showed the best results in the most impactful and stakeholder categories.This was primarily attributed to: i) the system simplicity, which consequently leads to an improvement in the health and safety concerns of workers; ii) the elimination of pollutants enhances health and safety, acceptableness, and odor effects for customers and the regional population; iii) the existence of robust laws, regulatory frameworks, and comprehensive implementation, which advantage value chain participants and society.These outcomes underscore the potential of integrated microalgae-biochar systems as a sustainable strategy for resource recovery, demonstrating significant reductions in environmental impacts across LCA categories.The study provides critical insights into scalable green technologies for wastewater valorization while mitigating ecological burdens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".