Sensitivity of PM2.5 to NOx emissions and meteorology in North China based on observations

Beixi Jia, Yuxuan Wang*, Chuanhui Wang, Qianqian Zhang, Meng Gao*, Kin Lam Yung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

11 Citations (Scopus)


This study examines the sensitivity of daily PM2.5 to NOx emissions and meteorology using in situ observations from main cities of North China (NC). NC cities are divided into low-, medium-, and high-emission groups by the ranking of their 4-year mean NO2. For each emission group, daily NO2 levels are used to divide the days into good-, medium-, and bad-meteorological conditions. Regardless of their emission levels, all cities reveal significant decreases (96%-172%) in daily PM2.5 levels from bad to good meteorological conditions. The largest difference in PM2.5 concentrations between the emissions groups is found under bad meteorological conditions, with 56% higher PM2.5 in high-emission cities than low-emission cities, indicating PM2.5 under bad meteorological conditions has the largest sensitivity to emissions. The high-emission, bad-meteorology group saw a 24% decrease in mean daily PM2.5 levels from 2017, a high-emission year, to 2019, a low-emission year. However, under good meteorological conditions, the high-emissions group shows an increase of 8.8 μg/m3 in mean daily PM2.5 from 2017 to 2019 with a 2.6% increase in the possibility of high PM2.5. These results suggest the current emission reduction measures are more effective in controlling PM2.5 in high-emission cities under bad meteorological conditions than under other meteorological conditions.

Original languageEnglish
Article number142275
JournalScience of the Total Environment
Early online date7 Oct 2020
Publication statusPublished - 20 Apr 2021

Scopus Subject Areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

User-Defined Keywords

  • Emission
  • Meteorology
  • North China
  • PM concentration


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