Atmospheric Inversion Approach‐Based Constraint on CH ₄ Emissions and Future Projections Under Climate Scenarios for Waste Treatment: A Case Study in Yangtze River Delta Region, China

Abstract China is the largest methane (CH 4 ) emitter globally, with the Yangtze River Delta (YRD) region recognized as a major emission hotspot. However, due to the scarcity of in situ observations and the complex spatiotemporal variability of these sources, significant uncertainties remain in regional CH 4 emission estimates. To address this, we conducted continuous atmospheric CH 4 concentration measurements from 1 June 2023, to 31 May 2024, at a central YRD site. Using an atmospheric transport model and a Bayesian inversion framework, we quantified monthly and sub‐monthly CH 4 emissions from different source categories, with a focus on waste treatment (including both landfill and wastewater). The results reveal the following key findings: (a) Substantial discrepancies were found between prior and posterior emissions across all categories. At the city scale, posterior annual CH 4 emissions were estimated to be 87.4%, 64.6%, 109.5%, and 91.9% of prior emissions for all categories, waste treatment, rice paddy + wetland, and other sources, respectively, with waste treatment contributing the largest uncertainty. (b) Strong seasonal biases were observed, with waste treatment emissions peaking in August and reaching a minimum in March (a 2.6‐fold variation), while rice paddy emissions were overestimated in May and underestimated in August by a factor of two. (c) CH 4 emissions from waste treatment exhibited high temperature sensitivity, increasing by 29%–31% per 10°C rise. Under future warming scenarios, waste treatment CH 4 emission factors (EFs) will increase by up to 121.3% under SSP5‐8.5 by the end of the century (2091–2100), relative to 2023–2024 levels. In contrast, atmospheric pressure showed negligible influence (3.2% per 1 hPa) on waste treatment CH 4 emissions. (d) More additional observations (i.e., satellite or multiple sites) are strongly suggested to resolve the prior spatial pattern of emissions, especially for fossil fuel‐related sources.