Comparison of different modeling approaches to better evaluate greenhouse gas emissions from whole wastewater treatment plants

New tools are being developed to estimate greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). There is a trend to move from empirical factors to simple comprehensive and more complex process-based models. Thus, the main objective of this study is to demonstrate the importance of using process-based dynamic models to better evaluate GHG emissions. This is tackled by defining a virtual case study based on the whole plant Benchmark Simulation Model Platform No. 2 (BSM2) and estimating GHG emissions using two approaches: (1) a combination of simple comprehensive models based on empirical assumptions and (2) a more sophisticated approach, which describes the mechanistic production of nitrous oxide (N2O) in the biological reactor (ASMN) and the generation of carbon dioxide (CO2) and methane (CH4) from the Anaerobic Digestion Model 1 (ADM1). Models already presented in literature are used, but modifications compared to the previously published ASMN model have been made. Also model interfaces between the ASMN and the ADM1 models have been developed. The results show that the use of the different approaches leads to significant differences in the N2O emissions (a factor of 3) but not in the CH4 emissions (about 4%). Estimations of GHG emissions are also compared for steady-state and dynamic simulations. Averaged values for GHG emissions obtained with steady-state and dynamic simulations are rather similar. However, when looking at the dynamics of N2O emissions, large variability (36?ton?CO2e?day-1) is observed due to changes in the influent wastewater C/N ratio and temperature which would not be captured by a steady-state analysis (4.4?ton?CO2e?day-1). Finally, this study also shows the effect of changing the anaerobic digestion volume on the total GHG emissions. Decreasing the anaerobic digester volume resulted in a slight reduction in CH4 emissions (about 5%), but significantly decreased N2O emissions in the water line (by 14%). Biotechnol. Bioeng. 2012; 109: 28542863. (c) 2012 Wiley Periodicals, Inc.