Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data

doi:10.1371/journal.pone.0238696

	Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data
	Yue, Yanlin; Wang, Zheng; Tian, Li; Zhao, Jincai; Lai, Zhizhu; Ji, Guangxing; Xia, Haibin
刊名	PLOS ONE
	2020
卷号	15
DOI	10.1371/journal.pone.0238696
英文摘要	Due to the rapid economic growth and the heavy reliance on fossil fuels, China has become one of the countries with the highest sulfur dioxide (SO2) emissions, which pose a severe challenge to human health and the sustainable development of social economy. In order to cope with the serious problem of SO(2)pollution, this study attempts to explore the spatial temporal variations of industrial SO(2)emissions in China utilizing the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. We first explored the relationship between the NSL data and the statistical industrial SO(2)emissions at the provincial level, and confirmed that there was a positive correlation between these two datasets. Consequently, 17 linear regression models were established based on the NSL data and the provincial statistical emissions to model the spatial-temporal dynamics of China's industrial SO(2)emissions from 1997 to 2013. Next, the NSL-based estimated results were evaluated utilizing the prefectural statistical industrial SO(2)emissions and emission inventory data, respectively. Finally, the distribution of China's industrial SO(2)emissions at 1 km spatial resolution were estimated, and the temporal and spatial dynamics were explored from multiple scales (national scale, regional scale and scale of urban agglomeration). The results show that: (1) The NSL data can be successfully applied to estimate the dynamic changes of China's industrial SO(2)emissions. The coefficient of determination (R-2) values of the NSL-based estimation results in most years were greater than 0.6, and the relative error (RE) values were less than 10%, when validated by the prefectural statistical SO(2)emissions. Moreover, compared with the inventory emissions, the adjusted coefficient of determination (Adj.R-Square) reached 0.61, with the significance at the 0.001 level. (2) During the observation period, the temporal and spatial dynamics of industrial SO(2)emissions varied greatly in different regions. The high growth type was largely distributed in China's Western region, Central region, and Shandong Peninsula, while the no-obvious-growth type was concentrated in Western region, Beijing-Tianjin-Tangshan and Middle south of Liaoning. The high grade of industrial SO(2)emissions was mostly concentrated in China's Eastern region, Western region, Shanghai-Nanjing-Hangzhou and Shandong Peninsula, while the low grade mainly concentrated in China's Western region, Middle south of Liaoning and Beijing-Tianjin-Tangshan. These results of our research can not only enhance the understanding of the spatial-temporal dynamics of industrial SO(2)emissions in China, but also offer some scientific references for formulating feasible industrial SO(2)emission reduction policies.
语种	英语
内容类型	期刊论文
源URL	[http://ir.casisd.cn/handle/190111/9820]
专题	中国科学院科技战略咨询研究院
推荐引用方式 GB/T 7714	Yue, Yanlin,Wang, Zheng,Tian, Li,et al. Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data[J]. PLOS ONE,2020,15.
APA	Yue, Yanlin.,Wang, Zheng.,Tian, Li.,Zhao, Jincai.,Lai, Zhizhu.,...&Xia, Haibin.(2020).Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data.PLOS ONE,15.
MLA	Yue, Yanlin,et al."Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data".PLOS ONE 15(2020).