Variable emission factors of CH4 and N2O from WWTPs: A model-based analysis on available data.

Abstract

Climate change and carbon emissions are increasingly becoming a global concern, and thus wastewater treatment plants (WWTPs) are also receiving extensive attention due to direct greenhouse gas (GHG) emissions of methane (CH₄) and nitrous oxide (N₂O). Although there have been many emission factors (EFs) of CH₄ and N₂O in literature, they are changeful due to different processes and boundaries, which limits their values for reference and comparison. With this study, in situ monitored CH₄ and N₂O data reported in literature were retrieved for recalculating their EFs. The average EFs are found to be 0.0011 g CH₄/g BOD_5-influent, and 0.0017 g N₂O-N/g TN_influent, based on the secondary treatment. Subsequently, the data were analyzed using multivariate linear regression and neural network. The results indicate that BOD₅ is the first factor affecting the EF of CH₄, revealing a negative correlation and that TN is the second factor affecting the EF of CH₄, but having a positive correlation. On the other hand, the neural network is a powerfully predictive and generalizable tool for EF_N2O. BOD₅ is negatively correlated with EF_N2O, and EF_N2O reaches to its maximum value at TN = 35 mg/L. Overall, the direct GHG emission intensity is the lowest in the AAO and AO processes, or with the BOD₅/TN ratio between 2.5 and 4.9. Medium-sized WWTPs and the Oceania region exhibit the highest GHG emission intensity. With this study, an approximate approach is established to estimate the EFs of CH₄ and N₂O, which can facilitate to account the carbon footprint of WWTPs and also to aid in optimizing their operational schemes.