Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities

Jianjun He, Sunling Gong, Ye Yu, Lijuan Yu, Lin Wu, Hongjun Mao, Congbo Song, Suping Zhao, Hongli Liu, Xiaoyu Li, Ruipeng Li

Research output: Contribution to journalArticlepeer-review

Abstract

In January 2013, the real-time hourly average concentrations of six pollutants (CO, NO2, O3, PM10, PM2.5and SO2) based on data from air quality monitoring stations in major Chinese cities were released to the public. That report provided a good opportunity to publicise nationwide temporal and spatial pollution characteristics. Although several studies systematically investigated the temporal and spatial trends of pollutant concentrations, the relation between air pollution and multi-scale meteorological conditions and their spatial variations on a nationwide scale remain unclear. This study analysed the air pollution characteristics and their relation to multi-scale meteorological conditions during 2014–2015 in 31 provincial capital cities in China. The annual average concentrations of six pollutants for 31 provincial capital cities were 1.2 mg m−3, 42.4 μg m−3, 49.0 μg m−3, 109.8 μg m−3, 63.7 μg m−3, and 32.6 μg m−3in 2014. The annual average concentrations decreased 5.3%, 4.9%, 11.4%, 12.0% and 21.5% for CO, NO2, PM10, PM2.5and SO2, respectively, but increased 7.4% for O3in 2015. The highest rate of a major pollutant over China was PM2.5followed by PM10, O3, NO2, SO2and CO. Meteorological conditions were the primary factor determining day-to-day variations in pollutant concentrations, explaining more than 70% of the variance of daily average pollutant concentrations over China. Meteorological conditions in 2015 were more adverse for pollutant dispersion than in 2014, indicating that the improvement in air quality was caused by emission controls.

Original languageEnglish
Pages (from-to)484-496
Number of pages13
JournalEnvironmental Pollution
Volume223
DOIs
Publication statusPublished - 22 Jan 2017

Keywords

  • Air pollution
  • Artificial neural network
  • Emission control
  • Meteorological conditions

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