Analysis of Urban Carbon Flux Patterns: A Case Study of Hefei Station

Cities are now central to global efforts to reduce CO 2 emissions, as they represent the major anthropogenic sources of carbon. Consequently, compiling urban carbon inventories, refining monitoring techniques, and investigating the distribution of sources and sinks in complex urban environments have become increasingly urgent. This study is based on data from an Eddy Covariance (EC) tower operated by the University of Science and Technology of China in downtown Hefei. The site represents a campus-influenced urban environment, with campus lawns and low-rise buildings in the near field and major roads in the broader footprint. By integrating high-resolution spatial data with flux-footprint modeling, we analyze the spatiotemporal patterns of urban CO 2 fluxes and their key drivers. Results show that CO 2 fluxes on weekdays exhibit a distinct bimodal diurnal cycle, while weekend emissions are 31.93% lower. Seasonal variability is governed by human activity and vegetation photosynthesis; winter emissions are 4.8-fold higher than those in summer. Spatial analysis attributes 4.36 kg CO 2 ·m -2 ·yr -1 to urban traffic, the dominant source in the study area. After gap-filling missing observations with an artificial neural network (ANN) and an improved marginal-distribution-sampling (iMDS) method, the annual regional emission is estimated at 4.81 kg CO 2 ·m -2 ·yr -1 . The fact that urban traffic emissions account for roughly 91% of the total indicates that non-traffic sources largely offset one another, such that, once traffic is excluded, the study area is close to a carbon-neutral state.