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Spatiotemporal heterogeneity relationships between atmospheric methane and various driving factors across China
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DOI：10.1016/j.atmosres.2026.109042.png)
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Abstract
En 中文
China is one of the largest emitters of methane (CH4). However, understanding the driving mechanisms behind its spatiotemporal variations remains uncertain due to diverse emission sources, uneven regional development, and complex natural conditions. This study investigates the influence of meteorological and anthropogenic driving factors on atmospheric CH4 concentrations in China from 2005 to 2020, utilizing Geographically Weighted Regression (GWR) and SHapley Additive exPlanations (SHAP) models. Both types of factors show significant spatial heterogeneity in their influence on CH4. The spatial relationship patterns between CH4 and anthropogenic factors exhibit distinct phased evolution, alongside shifts in policy interventions and economic transition, reflects the direct response of the atmospheric CH4 concentration variation to human activities. Meteorological factors have a particularly strong effect during summer, differing from other seasons. Regions characterized by high emissions and specific geographical conditions are more sensitive to meteorological changes. Temperature is the primary meteorological factor related to CH4 spatial distribution, exhibiting a nonlinear impact trend of increase-then-decrease. Among anthropogenic factors, changes in macro-level socioeconomic activities (i.e., consumption, population) are the strongest predictors of CH4 variations. Their overall influence is greater in southern regions and shows an initial increase followed by stabilization. SHAP feature importance values for direct emission sources show relatively small differences and are significantly lower than those of economic drivers. This underscores the need to focus on underlying drivers like consumption patterns, population growth, and economic development, rather than just direct emission sources, to understand CH4 emissions of China.
Keywords:
Methane
Spatiotemporal heterogeneity
Driving factors
GWR
SHAP
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