TY - JOUR
T1 - The spatiotemporal variation and key factors of SO2 in 336 cities across China
AU - Li, Rui
AU - Fu, Hongbo
AU - Cui, Lulu
AU - Li, Junlin
AU - Wu, Yu
AU - Meng, Ya
AU - Wang, Yutao
AU - Chen, Jianmin
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/2/10
Y1 - 2019/2/10
N2 - 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.
AB - 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.
KW - China
KW - Meteorological factors
KW - Socioeconomic factors
KW - Spatial econometric models
UR - https://www.scopus.com/pages/publications/85057173501
U2 - 10.1016/j.jclepro.2018.11.062
DO - 10.1016/j.jclepro.2018.11.062
M3 - 文章
AN - SCOPUS:85057173501
SN - 0959-6526
VL - 210
SP - 602
EP - 611
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
ER -