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oai:figshare.com:article/34022531 2026-09-29T10:04:48Z category_146 category_7 category_272 category_873 category_39 category_734 category_133 portal_63 item_type_6 month_year_09_2026

Breaking the “Measure-Model-Optimize” Bottleneck: A High-Resolution Spatial Framework for Reliable Wastewater Greenhouse Gas Management Yun-Peng Song (18810717) Zhi-Qiang Long (25136456) Qi-Wei Sun (25136459) Wen-Zhe Wang (10105287) Jia-Qiang Lv (19740665) Yu-Qi Wang (1538104) Wan-Xin Yin (19740662) Jia-Ji Chen (19740668) Tie-Fu Xu (25136462) Fang Ma (276816) Ai-Jie Wang (1957156) Hong-Cheng Wang (48817) Space Science Medicine Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Plant Biology train peaks corresponding specific digital twin resolution spatial framework reconstruct continuous ch optimize ” bottleneck maintaining effluent compliance introducing large uncertainties chamber flux measurements 74 – 28 6 – 17 15 – 13 ± 30 %. spatially explicit monitoring 2 </ sub based aeration optimization 4 </ sub 3 </ sup spatially heterogeneous guided optimization aeration transitions aeration baseline “ measure zoned dissolved simulated conventional scale anaerobic sampling indicated reduction technology oxic wrrf numerical sub national inventories meter scale longitudinal dissolved level control influent stripping greenhouse gas emission fields concentration mapping 64 kg 5 %. 08 kg 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. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.est.6c09219.s001 https://figshare.com/articles/journal_contribution/Breaking_the_Measure-Model-Optimize_Bottleneck_A_High-Resolution_Spatial_Framework_for_Reliable_Wastewater_Greenhouse_Gas_Management/34022531 CC BY-NC 4.0