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Title: Large-scale meteorological control on the spatial pattern of wintertime PM2.5 pollution over China

Journal Article · · Atmospheric Science Letters
DOI:https://doi.org/10.1002/asl.938· OSTI ID:1578053
ORCiD logo [1];  [2];  [2];  [3];  [4];  [5];  [2]
  1. Peking Univ., Beijing (China)
  2. Univ. of California, Los Angeles, CA (United States)
  3. Univ. of California, Los Angeles, CA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Shandong Univ., Jinan (China)
  5. Tsinghua Univ., Beijing (China)

The frequent episodes of severe air pollution over China during recent years have posed serious health threats to densely populated eastern China. Although several studies investigated the linkage between enhanced severity and frequency of air pol-lution and the large-scale weather patterns over China, the day-to-day covariability between them, as well as its local and remote mechanisms, has not been systemati-cally documented. The wintertime synoptic covariability between PM2.5 and large-scale meteorological fields is studied using surface observations of PM2.5 in 2013/2014–2016/2017 and ERA-Interim meteorological fields through maximum covariance analysis (MCA). The first MCA mode (MCA1) suggests a consistent accumulation of ambient PM2.5 as a result of weakened winds that block the pollut-ant removal passage in heavily polluted areas of eastern China, as well as moist air from southeast coast favoring haze formation. A northeast–southwest belt that extends into northeastern China and central China on each end is more sensitive to MCA1. The second MCA mode (MCA2) shows a north–south dipole in PM2.5 linked to the contrast of boundary layer height and surface wind speed between northern and southern regions of China. Spatial patterns of both modes are supported by the GEOS-Chem chemistry transport model with realistic emission inventory. The spatial patterns of the two modes are robust on the interannual time scales. Based on that, we investigate the variability of the first two modes of the identified modes on the multidecadal scale by projecting GPM_500 pattern to 1981–2010. Correlation analysis of the projected time series and climate indices over 30 years indicates the possible linkage of Arctic oscillation, ENSO indices, Pacific decadal oscillation and east Atlantic/western Russia to regional air pollution patterns over China.

Research Organization:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE; National Science Foundation (NSF)
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1578053
Report Number(s):
PNNL-SA-149479
Journal Information:
Atmospheric Science Letters, Vol. 20, Issue 10; ISSN 1530-261X
Publisher:
Royal Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

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