Quantitative analysis of carbon emissions and material flow of a full-scale wastewater treatment plant with AAO process

Given the increasing attention in achieving carbon neutrality, carbon emissions and material flow are critical for the sustainability of wastewater treatment plants (WWTPs). This research firstly integrated carbon emission accounting, material flow analysis (MFA), and machine learning-based optimization to characterize a full-scale WWTP with the anaerobic-anoxic–oxic process. It was found that the annual carbon emission intensity was 0.734 ± 0.069 kgCO₂·m⁻³, with direct and indirect emissions contributing 36.8% and 63.2%, respectively. Notably, the anaerobic tank was identified as the primary source of direct emissions from nitrous oxide and methane production, while the aerobic tank was the largest contributor of indirect emissions, accounting for about 40% of the total electricity consumption. Additionally, nitrogen and organic load had a significant impact on direct emissions, while the poly aluminium chloride dosage had the largest effect on chemical consumption-related carbon emissions (r = 0.853, p < 0.001). Innovatively, machine learning was employed to predict the full-process carbon emissions of the plant and then the differential evolution algorithm was used to optimize the operation of the plant, resulting in 30.36% reduction of carbon emissions, 33.19% reduction of operating costs and 25.21% reductions of pollutants for per ton of water. MFA revealed distinct removal patterns for the three pollutants: (1) chemical oxygen demand—12.49% removed in pretreatment, 81.34% in biological treatment, with relatively low carbon recovery potential; (2) total nitrogen—15.35% removed in pretreatment, 62.00% in biological treatment, and 18.26% discharged with effluent; (3) total phosphorus—80.94% removed in the aerobic tank through polyphosphate-accumulating organisms with an additional 18.76% removed by coagulants in sedimentation, indicating high phosphorus recovery potential through sludge. Based on these material flow characteristics showing high nitrogen and phosphorus recovery potential, a multi-algae system composed of Spirulina and freshwater Chlorella with intelligent control was proposed in an embedded algal pond to maximize nitrogen and phosphorus recovery while serving as a carbon sink, thereby reducing energy consumption and chemical usage. In summary, this study provides valuable insights for carbon reduction, pollution mitigation, and resource recovery in urban WWTPs.