Recent intensification of winter haze in China linked to foreign emissions and meteorology [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2018.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- Article numbers 2,107 : digital, PDF file
- Additional Creators:
- Pacific Northwest National Laboratory (U.S.), United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Wintertime aerosol pollution in Northern China has increased over the past several decades as anthropogenic emissions in China have increased, and has increased dramatically since the beginning of the 21st century, but the causes and their quantitative contributions remain uncertain. Here we use an aerosol source tagging capability implemented in a global aerosol-climate model to assess long-term trends of PM2.5 (particulate matter less than 2.5 μm in diameter) in Northern China. Our analysis suggests that increasing PM2.5 concentrations due to local emission increases within China were obscured (~13%) by foreign emission reductions between 1980–2000. As foreign emissions stabilized during 2000-2014, their counteracting effect almost disappeared, uncovering China’s pollution potential from domestic emission increases. The meteorology dominated PM2.5 trend during 1990–1996 and also uncovered the pollution potential due to decadal variations in winds. The stabilized foreign emissions together with changing meteorology explain a quarter of the larger increasing trend of PM2.5 since the beginning of the 21st century. Future foreign emissions are not expected to help hiding China’s pollution, reductions in local emissions are the efficient way to improve future air quality in Northern China.
- Report Numbers:
- E 1.99:pnnl-sa--128267
- Published through SciTech Connect.
Scientific Reports 8 1 ISSN 2045-2322 AM
Yang Yang; Hailong Wang; Steven J. Smith; Rudong Zhang; Sijia Lou; Yun Qian; Po-Lun Ma; Philip J. Rasch.
- Funding Information:
View MARC record | catkey: 23774264