TY - JOUR
T1 - Sensitivity of PM2.5 to NOx emissions and meteorology in North China based on observations
AU - Jia, Beixi
AU - Wang, Yuxuan
AU - Wang, Chuanhui
AU - Zhang, Qianqian
AU - Gao, Meng
AU - Yung, Kin Lam
N1 - Funding Information:
This research was supported by the Natural Science Foundation of Guangdong Province ( 2019A1515011633 ) and the National Natural Science Foundation of China ( 41805098 ).
Copyright © 2020 Elsevier B.V. All rights reserved.
PY - 2021/4/20
Y1 - 2021/4/20
N2 - 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.
AB - 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.
KW - Emission
KW - Meteorology
KW - North China
KW - PM concentration
UR - http://www.scopus.com/inward/record.url?scp=85092757171&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2020.142275
DO - 10.1016/j.scitotenv.2020.142275
M3 - Journal article
C2 - 33077214
AN - SCOPUS:85092757171
SN - 0048-9697
VL - 766
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 142275
ER -