Spatiotemporal variations of carbon dioxide (CO₂) at Urban neighborhood scale: Characterization of distribution patterns and contributions of emission sources

The gradual increase in atmospheric carbon dioxide (CO₂) concentrations has attracted worldwide attention for its strong relationships with global climate change. Considerable efforts are being undertaken to characterize spatiotemporal variations of CO₂ at a city, regional and national level, aiming at providing pipelines for carbon emission reduction. However, there is scarce knowledge of how CO₂ at the urban neighborhood scale is produced and distributed in the context of time and space, which is useful to accurately target source contributions from the ground up and reduce carbon emissions at a fine-grained scale. In this study, mobile measurements of CO₂ concentrations were made in a 2 km × 2 km urban area covering different land use types to separately characterize the spatiotemporal distribution patterns of CO₂ in roadside, residential and green space areas. The results show that CO₂ concentrations in the late afternoon (Local time, UTC+8, LT 17–18) were higher than those at noon (LT 11–12), and that CO₂ concentrations in winter were higher than those in summer. The roadside areas exhibited the highest CO₂ concentration level of 452.66 ± 20.59 ppm, followed by residential areas (436.34 ± 27.02 ppm) and green space areas (428.98 ± 20.49 ppm). The result indicates that traffic sources brought more carbon emissions and contributed to a significant increase in CO₂ concentrations, while urban greenery caused more carbon absorptions and reduced CO₂ concentrations. This can be further confirmed by the observations that CO₂ concentrations in the roadside neighborhood showed a strong positive correlation (R² = 0.86) with ambient traffic flow. Then two machine learning models, i.e., Random Forest and eXtreme Gradient Boost, were developed to quantify the individual contribution from different carbon emission sources to the CO₂ distributions, including traffic flow, greening rate, and domestic energy consumption. The results show that traffic-related carbon emissions were the most important influencing factor and accounted for approximately 60% of ambient CO₂ concentrations, followed by greening rate (20%) and domestic energy consumption (10%). These findings can provide insights into spatiotemporal distributions and source contributions of CO₂ in urban neighborhoods and show huge potentials for reducing urban carbon emissions at a fine-grained scale.