Breaking the "Measure-Model-Optimize" Bottleneck: A High-Resolution Spatial Framework for Reliable Wastewater...

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.