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The spatiotemporal variation and key factors of SO2 in 336 cities across China

  • Rui Li
  • , Hongbo Fu*
  • , Lulu Cui
  • , Junlin Li
  • , Yu Wu
  • , Ya Meng
  • , Yutao Wang
  • , Jianmin Chen
  • *Corresponding author for this work
  • Fudan University
  • Tongji University
  • Nanjing University of Information Science & Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Sulfur dioxide (SO2) pollution has become a severe concern in China, which is closely linked to human health. Here, the officially released data of SO2 in the 336 prefecture-level cities in 2015 across the whole China were firstly collected to understand the spatiotemporal variation of the SO2 concentration. At a national scale, the SO2 concentration was highest in winter, followed by one in spring and autumn, and the lowest one in summer. The spatial econometric models, the geographical weight regression (GWR) model, and the generalized additive model (GAM) were then applied to examine the interaction of socioeconomic factors (e.g., gross domestic production (GDP)) and the meteorological indicators (e.g., precipitation) on the SO2 level in the 336 cities over China. The results suggested that the SO2 concentration was negatively associated with GDP, precipitation, wind speed (WS), and relative humidity (RH), while it showed the positive relationship with gross industrial production (GIP), population, and temperature. GDP in the Jiangsu and Zhejiang provinces presented the negative correlations with the SO2 concentration, suggesting the adaptation of industrial structure has occurred in the developed region. The positive effect of GIP on the SO2 concentration increased from West China to North China because many energy-intensive industries were concentrated on North China. The GAM analysis suggested that the combined effects of the adverse meteorological condition (e.g., RH = 50–60%) and the higher GIP contributed to severe SO2 pollution. Therefore, the SO2 emission from the heavy industries especially in NCP should be reduced and many energy-intensive plants in the region should be moved to some cities with favorable diffusion condition.

Original languageEnglish
Pages (from-to)602-611
Number of pages10
JournalJournal of Cleaner Production
Volume210
DOIs
StatePublished - 10 Feb 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  3. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • China
  • Meteorological factors
  • Socioeconomic factors
  • Spatial econometric models

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