Breaking the “Measure-Model-Optimize” Bottleneck: A High-Resolution Spatial Framework for Reliable Wastewater Greenhouse Gas Management

Abstract

Non-CO2 greenhouse gas (GHG) emissions from urban wastewater resource recovery facilities (WRRFs) are spatially heterogeneous at the meter scale, introducing large uncertainties in national inventories and process-level control. We establish SMART-GHG (Spatially explicit Monitoring, Assessment, and Reduction Technology for Greenhouse Gas management), combining unmanned-aerial-vehicle (UAV) concentration mapping with floating-chamber flux measurements to reconstruct continuous CH4 and N2O emission fields. The framework quantifies sampling requirements and validates an activated-sludge model as a facility-specific digital twin. At a full-scale anaerobic/anoxic/oxic WRRF (300,000 m3 d–1), seasonal surveys of the monitored basin in one of four parallel treatment trains yielded CH4 emissions of 3.15–13.08 kg d–1, dominated by influent stripping, and N2O emissions of 4.74–28.64 kg d–1, with mid-train peaks corresponding to aeration transitions. Numerical sub-sampling indicated that stable emission estimates required approximately 30 measurement points per lane (5 m spacing) in this basin; sparser sampling induced errors up to ±30%. Validation against longitudinal dissolved-oxygen and nitrogen profiles yielded mean absolute percentage errors of 3.6–17.5%. Model-guided optimization of zoned dissolved-oxygen set points predicted approximately 12% lower N2O emissions and 9% lower aeration energy use than a simulated conventional-aeration baseline, while maintaining effluent compliance in the model. SMART-GHG supports spatially explicit emission inventories and model-based aeration optimization in WRRFs.