Analysis of empirical methods for the quantification of N2O emissions in wastewater treatment plants: Comparison of emission results obtained from the IPCC Tier 1 methodology and the methodologies that integrate operational data

Abstract Wastewater is a source of N2O emission that is generated, both directly from advanced treatment plants and indirectly from the discharge of wastewater into the natural environment, due to its remaining nitrogen content. There are a variety of methods based on different parameters used to calculate N2O emission in wastewater treatment plants. The methodology proposed by the IPCC is used as an international reference for national inventories. In this work, we use five international methodologies to calculate the N2O emission of the WWTPs in two areas with high population density: The Metropolitan Area of Barcelona (MAB) and Mexico City (MXC). The MAB has 100% population served and has advanced treatment plants (five WWTP) and traditional wastewater treatment plants (two WWTP), the MXC served 14% of its population and had advanced treatment plants (six WWTP) and traditional plants (nineteen WWTP) in 2016. The results obtained show that the IPCC and Das methodologies underestimate the emission of N2O by considering the per capita consumption of proteins as a constant nitrogen value and also by the suggested emission factors. The methodologies that use the operational data of each plant provide emission results closer to those found in the literature. The value of TN should be the parameter to be considered for a correct estimate of the N2O emission in the WWTPs. The emission factors currently used are very low, with a low level of confidence of up to 1.3%. The range currently used should be increased and have a minimum range of 0.03 kg N2O-N/kg N. The emission factors reported in the literature are very variable and with very high levels of uncertainty, and therefore underestimate the emission of N2O in WWTPs. More research should be done to obtain higher and more reliable emission factors than those currently used.