Improving urban CO 2 spatial distribution modelling using multi-source data

Urban areas contribute approximately 70% of the total anthropogenic CO 2 emissions, making them the primary focus of global carbon monitoring. However, accurate modelling of urban meteorology is challenging because of complex artificial landscapes. Reducing errors in urban meteorological simulation and improving the accuracy of modelling the spatial distribution of urban CO 2 is of great significance for the " top -down " CO 2 emissions estimation in urban areas. In this study, local urban datasets were constructed based on multiple data sources to overcome the limitation of insufficient information from a single data source. In the development of urban datasets, grid -by -grid data processing is realized, and the evaluation results show that the developed urban datasets are greatly improved compared with the default. The developed urban datasets were applied to WRF-Chem with 1 -km spatial resolution, and the simulated columnaveraged dry air mole fraction of CO 2 (XCO 2 ) was verified with the EM27/SUN observed data considering the slant observation path. The results show that urban datasets strongly influence the spatial distribution modelling of XCO 2 , depending on the state of the atmosphere near the surface, especially wind velocity. Accurate local urban datasets can effectively reduce the bias in urban XCO 2 spatial distribution modelling and improve the capability of the atmospheric CO 2 transport model in urban carbon emission estimation.