Regionalization based on spatial and seasonal variation in ground-level ozone concentrations across China

J Environ Sci (China). 2018 May:67:179-190. doi: 10.1016/j.jes.2017.08.011. Epub 2017 Aug 30.

Abstract

Owing to the vast territory of China and strong regional characteristic of ozone pollution, it's desirable for policy makers to have a targeted and prioritized regulation and ozone pollution control strategy in China based on scientific evidences. It's important to assess its current pollution status as well as spatial and temporal variation patterns across China. Recent advances of national monitoring networks provide an opportunity to insight the actions of ozone pollution. Here, we present rotated empirical orthogonal function (REOF) analysis that was used on studying the spatiotemporal characteristics of daily ozone concentrations. Based on results of REOF analysis in pollution seasons for 3years' observations, twelve regions with clear patterns were identified in China. The patterns of temporal variation of ozone in each region were separated well and different from each other, reflecting local meteorological, photochemical or pollution features. A rising trend in annual averaged Eight-hour Average Ozone Concentrations (O3-8hr) from 2014 to 2016 was observed for all regions, except for the Tibetan Plateau. The mean values of annual and 90 percentile concentrations for all 338 cities were 82.6±14.6 and 133.9±25.8μg/m3, respectively, in 2015. The regionalization results of ozone were found to be influenced greatly by terrain features, indicating significant terrain and landform effects on ozone spatial correlations. Among 12 regions, North China Plain, Huanghuai Plain, Central Yangtze River Plain, Pearl River Delta and Sichuan Basin were realized as priority regions for mitigation strategies, due to their higher ozone concentrations and dense population.

Keywords: Ozone; REOF; Regionalization; Spatiotemporal variability.

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / statistics & numerical data*
  • China
  • Cities
  • Climate
  • Environmental Monitoring*
  • Ozone / analysis*
  • Rivers
  • Seasons

Substances

  • Air Pollutants
  • Ozone