Life cycle assessment of biological nutrient removal considering seasonal variations in environmental impacts

Wastewater treatment plays a critical role in mitigating environmental impacts. However, the environmental performance of treatment processes varies depending on operational configurations and external factors such as seasonal changes. This study aimed to investigate three wastewater treatment plants (WWTPs) with different biological nutrient removal (BNR) configurations -anaerobic-anoxic-oxic (A2O), five-stage Bardenpho, and Johannesburg processes- using the life cycle assessment (LCA) approach to comparatively examine their environmental performances and to conduct an LCA study using two functional units (FUs) (1 m³ of wastewater treated and 1 kg of total nitrogen (TN) removed), revealing seasonal environmental impacts for A2O process to reveal the difference in interpretation according to the FU selected. The ReCiPe 2016 methodology was employed, focusing on ten key impact categories, including global warming, marine eutrophication, freshwater eutrophication, human carcinogenic toxicity, human non-carcinogenic toxicity, marine ecotoxicity, freshwater ecotoxicity, terrestrial ecotoxicity, terrestrial acidification, and fossil resource scarcity. The results show that the Johannesburg process achieves better environmental performance compared to the others. The superior performance of the Johannesburg process can be attributed to having no fugitive methane (CH₄) emissions due to the absence of anaerobic digester in the sludge treatment and its competitive TN removal rates, which align directly with sustainability objectives like emission reduction and reducing carbon footprint. Choosing the right FU is crucial for sustainability. The FU based on 1 kg TN removed provided a more representative view of environmental efficiency under changing seasonal conditions. These findings emphasize the importance of aligning FU selection with sustainability goals. In terms of wastewater management policy, the study suggests that monitoring based on the percentage of pollutant load removed or total annual emissions generated would be more meaningful, taking into account changing external conditions such as seasonal factors.