Abstract

With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi’an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles.

Highlights

  • With the rapid growth of the economy and urbanization, the high concentration of fine particulate matter (PM2.5 ) has been frequently occurring in many regions of China [1,2,3].Considering that high PM2.5 concentrations worsen air quality and visibility [4,5], and threaten human health and the regional climate [6,7,8], the Chinese government has engaged in reducing the annual average PM2.5 concentrations to 35 μg/m3 by 2030 to meet the requirement by the World Health Organization (WHO) during the “transition period”

  • The primary PM2.5 came from exhaust emissions, brake wear, and tire

  • The primary PM2.5, nitrogen oxide (NOx), and SO2 emissions from vehicles were calculated by employing the Motor Vehicle Emission Simulator (MOVES) model, and the spatial distribution characteristics of PM2.5 concentrations and the contributions of vehicle-related emission sources to the total PM2.5 concentrations were investigated by utilizing a Weather Research and Forecasting (WRF)/California Puff (CALPUFF) modeling system

Read more

Summary

Introduction

Considering that high PM2.5 concentrations worsen air quality and visibility [4,5], and threaten human health and the regional climate [6,7,8], the Chinese government has engaged in reducing the annual average PM2.5 concentrations to 35 μg/m3 by 2030 to meet the requirement by the World Health Organization (WHO) during the “transition period”. To achieve this goal, it is important to investigate the influences of anthropogenic sources on ambient PM2.5 concentrations. Vehicle emissions accounted for 16%, 25%, and 26% of the ambient PM2.5 concentrations in New

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call