Searcharxiv⌕ Search

arXiv subjects

William S. Daniels

Publications and source records attributed to William S. Daniels.

2 recordsLinked to original sources

Satellite-based emissions estimate indicates progress toward China's methane mitigation goals

China emits the most methane of any country worldwide, but there are large uncertainties in recent emissions trends, sources, and the potential impacts of policy actions. This study focuses on a period when the government initiated ambitious methane control efforts, linking sectoral policies with atmospheric evidence on sectoral, sub-national, and seasonal emissions during 2019--2024. We quantify daily methane emissions from China using a regional atmospheric inverse model with TROPOMI satellite observations. Our results reveal an average methane emissions increase rate of 0.3 Tg yr$^{-2}$ in Eastern & Central China, likely a milder trend than in the 2010s. Coal industry methane emissions intensity declined for the first time (-3.2% yr$^{-1}$) despite rising production, possibly associated with diverse policy instruments, mandates, and incentives. We further highlight two emerging challenges for future mitigation: leaks from expanding urban gas use amid the energy transition and rising agricultural emission yet with substantial uncertainty in estimates. Lastly, declining emissions intensity of coal mines points to the future role of targeted mandates and incentives in encouraging methane reduction for other sectors.

physics.ao-ph↗

A Bayesian hierarchical model for methane emission source apportionment

Reducing methane emissions from the oil and gas sector is a key component of short-term climate action. Emission reduction efforts are often conducted at the individual site-level, where being able to apportion emissions between a finite number of potentially emitting equipment is necessary for leak detection and repair as well as regulatory reporting of annualized emissions. We present a hierarchical Bayesian model, referred to as the multisource detection, localization, and quantification (MDLQ) model, for performing source apportionment on oil and gas sites using methane measurements from point sensor networks. The MDLQ model accounts for autocorrelation in the sensor data and enforces sparsity in the emission rate estimates via a spike-and-slab prior, as oil and gas equipment often emit intermittently. We use the MDLQ model to apportion methane emissions on an experimental oil and gas site designed to release methane in known quantities, providing a means of model evaluation. Data from this experiment are unique in their size (i.e., the number of controlled releases) and in their close approximation of emission characteristics on real oil and gas sites. As such, this study provides a baseline level of apportionment accuracy that can be expected when using point sensor networks on operational sites.

stat.AP↗