Enhancing vertical and temporal allocation of freeway traffic emissions to improve air quality modeling in Taiwan

Accurate representation of on-road emissions is critical for improving air quality simulations, particularly in regions with complex roadway structures and dynamic traffic patterns. Conventional chemical transport models (CTMs) typically allocate all vehicle emissions to the surface layer and apply fixed temporal profiles, which can introduce systematic biases in simulated pollutant concentrations, including overestimation of near-surface NO_x and distortion of the O₃ titration process. In this study, we develop an improved emission allocation framework that incorporates both vertical redistribution of freeway emissions based on roadway elevation data and temporal adjustments accounting for holiday-related traffic variability. The framework is implemented within the WRF-CMAQ modeling system over Taiwan. Model evaluation against surface observations demonstrates that the proposed approach yields measurable improvements in simulation performance. Winter simulations (December 2022-February 2023) indicate that approximately 29.8% of freeway emissions are redistributed to elevated layers. This vertical reallocation reduces near-surface NO_X concentrations by 2.8 ppb in daily mean values, mitigates excessive O₃ titration in urban areas, and influences PM_2.5 formation, particularly near major freeway networks. Incorporating holiday-adjusted temporal profiles further improves model performance, reducing the average NO_X overestimations from 1.94 ppb to 1.04 ppb during extended holidays, with the greatest improvements observed in areas with dense roadway infrastructure. Overall, this study demonstrates that incorporating more realistic vertical and temporal emission representations can substantially improve CTM performance, highlighting their importance for air-quality modeling and policy applications.