Quantifying black carbon emission from on-road mobile sources in an urban area using mobile measurement and CFD modeling

 Mobile measurement methods, including unmanned aerial vehicle (UAV) and mobile laboratory, are gaining attention as effective tools for addressing underestimation issues in statistical emission inventories caused by unidentified emission sources. This study aims to estimate black carbon (BC) emissions from roadways in urban areas using mobile measurement techniques. We focuses on a residential area near Olympic-daero in Seoul in January and May, 2021. BC concentrations were measured at 1-second intervals using mini aethalometer (MA200) mounted on a bicycle and UAV to capture spatial distributions and background concentrations. Wind fields were simulated using a Computational Fluid Dynamics (CFD) model. The collected measurement data were then combined with the simulated wind fields and analyzed using a box model based on the mass balance equation to estimate BC emissions from roadways. For comparison, BC emission from on-road mobile sources were statistically calculated using national emission factors and traffic volume data obtained in the study area. The mobile measurement-based emission amounts were approximately twice as high as the statistically estimated emission amounts. Furthermore, the CFD simulation using mobile measurement-based emissions showed a higher accuracy compared to the CFD simiulation using statistically estimated emissions. This study demonstrates that a mobile measurement-based method for estimating emissions can complement a statistical estimation of emission from a specific target source. It will help us improve emission inventories more accurately.

This research was supported in part by the Technology Development Program to Solve Climate Change through the National Research Foundation of Korea (NRF) (2019M1A2A2103954) and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2024-00356913).