• UAV, flux chamber, and point source measurements of facility-wide CH 4 emissions • UAV-derived CH 4 emissions were lower than chamber and point source based totals • Flare and biosolids drying accounted for 86% and 14% of total CH 4 emissions • Emission variability governed by unit process followed by season and time of day • Operational recommendations are provided to mitigate gas generation and emissions This study presents a facility-wide assessment of greenhouse gas (CH₄, N₂O, CO₂) and air-pollutant (NH₃, H₂S, NMVOC) emissions from a tertiary municipal wastewater treatment plant in northern California. Emissions were quantified using complementary unmanned-aerial-vehicle (UAV) flux-curtain, dynamic flux-chamber, and source-testing approaches across four campaigns. The UAV method provided spatially integrated, top-down CH₄ fluxes, while chambers and source tests captured spatiotemporal variability of multiple gases from individual processes. The highest UAV flux (26.4 ± 6.3 kg CH₄ h⁻¹) in the first campaign coincided with a fugitive digester leak detected and later repaired, demonstrating UAV sensitivity to large, transient CH₄ releases. Across the remaining campaigns, mean UAV-derived facility-wide emissions were 1.44 kg CH₄ h⁻¹, approximately 90% lower than the ∼13.3 kg CH₄ h⁻¹ mean facility-wide emissions estimated from summed flux chamber and point-source measurements. Total operational-carbon emissions, integrating direct and indirect forcing contributions, were 12,000 ± 500 Mg CO₂-eq yr⁻¹ (1.80 ± 0.07 kg CO₂-eq m⁻³ wastewater). The candlestick flare and biosolids drying operations accounted for 86 % and 14 % of total CH₄ emissions, while secondary treatment dominated CO₂, NH₃, and N₂O generation. Emission variability was governed primarily by unit process, followed by time of year (e.g., wet or dry season) and time of day, with higher fluxes during dry-season daytime conditions. The integrated UAV, chamber, and source testing framework established herein provides a novel, multi-scale approach for quantifying and mitigating facility-wide GHG and air-pollutant emissions. The methodology and mechanistic insights are transferable to wastewater utilities globally, supporting data-driven, low-carbon treatment design and operation.
Manheim et al. (Wed,) studied this question.