Coupling Long-Term Ground-Based Flux Measurements with a Lagrangian Transport Model to Quantify and Attribute Emissions in a Coal Mining Cluster

Accurate quantification of greenhouse gas (GHG) emissions from coal mining activities is crucial for developing effective mitigation strategies and achieving carbon neutrality goals. This study presents a multi-month (starting August 2025) experimental campaign conducted in a prominent coal mining cluster. We deployed a high-precision ground-based observation tower (70m a.g.l) to monitor continuous atmospheric concentrations and calculate fluxes of CH₄, CO₂ at DaBuTou station (36.07˚N, 112.88˚E, hereafter DBT). The DBT station is located in Zhangzi, Changzhi, Shanxi Province, and has several coal mines within a 10-km radius in all directions of the observation site. An LI-7700 Open-Path CH₄ Gas Analyzer (LI-COR, Inc.) was mounted at a 65.2 m height on the tower, along with an Integrated CO₂/H₂O Open-Path Gas Analyzer and 3D Sonic Anemometer (IRGASON, Campbell Scientific, Inc.). To bridge the gap between observed concentrations and source strengths, the Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport (STILT) model was employed. The WRF-STILT framework was used to generate high-resolution footprints, characterizing the sensitive source areas contributing to the tower flux and concentration measurements.

Preliminary results reveal significant diurnal variations in methane footprints, driven by complex terrain and fluctuating operational intensities within the coal-mining cluster. Height selection fundamentally dictates the spatial representativeness of specific mining activities within the cluster, providing a critical benchmark for optimizing emission estimate model’s parameters to ensure that flux measurements are strategically weighted toward key industrial emitters. We note some interesting conclusions: first that it is possible to separate some of the various coal mine sources from each other using a sufficiently long dataset; and second that observational uncertainty spans both concentration and wind observations in tandem, meaning that simple approaches for emissions estimation are insufficient; and finally that a very small number of days have a substantial difference in terms of emissions from the other days, requiring that observations be conducted very long-term before annual or other types of climatological conditions can be established.

In conclusion, this research provides a robust framework for utilizing direct CH₄ flux measurements to characterize fugitive emissions in coal-mining clusters. Our findings establish a verifiable 'ground-truth' framework that not only refines regional emission inventories but also serves as a critical diagnostic tool for industrial stakeholders and regulatory agencies to implement verifiable GHG reduction pathways and advance toward net-zero climate goals.