Quantifying the effect of driving restrictions on fine particulate matter concentrations with WRF-Chem model: A case study in Kaifeng, China

Abstract

One of the significant sources of urban air particulate matter (PM_2.5 and PM₁₀) has been found to be vehicle exhaust emission. Vehicle restriction measures have been taken to alleviate particulate matter pollution in the world. Here, we quantified the impact of vehicle restriction measures on the PM_2.5 concentrations using the Weather Research and Forecasting model with Chemistry (WRF-Chem) in Kaifeng, China. This study set up three different scenarios to simulate the spatiotemporal changes of PM_2.5 concentrations in four seasons in 2020. They were no emission reduction (S0), the restriction of two tailing license plate numbers (S1), and the restriction of odd–even tailing numbers of license plates (S2), respectively. In general, the odd–even license plate restriction policy has proven to be more effective than restricting two specific license plate numbers. In scenario S1, the range of PM_2.5 concentrations reduction in Kaifeng City was between 0.2% and 1.28%. In scenario S2, the range of PM_2.5 concentrations reduction was between 0.98% and 14.42%. The results showed that the effectiveness of vehicle restriction measures varies from season to season, and the effect in summer and winter is better than that in spring and autumn. We suggest that it may be difficult to reduce the PM_2.5 concentrations to a great extent if simply reducing the traffic exhaust emissions through motor vehicle restrictions, on the contrary, would lead to the increase of O₃ concentration, bringing some other effects. This study can serve as a foundation for the rational development of motor vehicle control strategies and help advance the ongoing improvement of air quality.