Scaling Urban Methane Emissions: Utility of Single-Site Measurements in Five Urban Domains

Urban methane (CH₄) missions remain poorly understood due to limited observational constraints. Most estimates rely on bottom-up inventories based on assumed emission factors and activity data or downscaling methods, which often underestimate emissions, sometimes by a factor of 2 or more in United States and European cities. While satellite and mobile observations can improve understanding, they face limitations in spatial resolution, coverage, and frequency. In contrast, fixed in situ measurements calibrated to World Meteorological Organization standards offer high precision continuous data, although with limited spatial coverage due to logistical constraints. This study uses in situ observations from single tower sites in five northeastern United States cities to estimate total urban CH₄ emissions using a Bayesian scaling factor framework. Despite limited spatial sampling, the approach yields robust emission estimates consistent with other studies. To explore drivers of variability, the analysis examines correlations between inferred emissions and urban characteristics including population, residential gas usage, and infrastructure. Results show that residential building volume outperforms population as a predictor in some regions, highlighting the importance of infrastructure-specific factors. By demonstrating a scalable observation-based approach using minimal sites, this work addresses key gaps in urban CH₄ monitoring and emphasizes the value of robust measurements and tailored proxies for improving emission estimates in diverse urban settings.