High-Resolution Mapping and Interpretation of Stable Urban Surface CO2 Concentration Patterns Using CSF-Processed Mobile Observations and Multiscale Remote Sensing in Shenzhen, China

High-resolution mapping of urban surface CO2 is essential for refined carbon monitoring, emission management, and low-carbon urban planning. Mobile monitoring provides dense street-level observations, but raw CO2 measurements are often affected by transient traffic disturbances, vehicle idling, and localized plume events, which limits their direct use as stable spatial mapping targets. This study developed an integrated framework for predicting, mapping, and interpreting stable surface CO2 patterns in Shenzhen by combining vehicle mobile observations, CSF processing, multiscale remote sensing predictors, machine learning. A CSF-based lower-envelope filter was used to suppress short-duration positive peaks and extract a more stable CO2 accumulation signal from mobile observations. Multiscale predictors representing transportation, urban activity, surface environment, and built form were constructed to characterize both local and surrounding urban contexts. Compared with raw CO2, the CSF-processed target substantially improved prediction performance. The best validation R2 across the candidate models increased from 0.59 to 0.90 in April and from 0.62 to 0.93 in November. The predicted maps identified persistent high-CO2 areas in central and southwestern Shenzhen. SHAP results showed that transport networks and urban activity reinforced surface CO2 accumulation, whereas vegetation and open-surface contexts weakened accumulation at broader spatial ranges. These findings provide an interpretable framework for high-resolution urban CO2 mapping and refined low-carbon governance.