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Modeling the spatiotemporal dynamics of industrial sulfur dioxide emissions in China based on DMSP-OLS nighttime stable light data.
- Source :
- PLoS ONE; 9/10/2020, Vol. 15 Issue 9, p1-20, 20p
- Publication Year :
- 2020
-
Abstract
- 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 (SO<subscript>2</subscript>) 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<subscript>2</subscript> pollution, this study attempts to explore the spatial temporal variations of industrial SO<subscript>2</subscript> 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<subscript>2</subscript> 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<subscript>2</subscript> emissions from 1997 to 2013. Next, the NSL-based estimated results were evaluated utilizing the prefectural statistical industrial SO<subscript>2</subscript> emissions and emission inventory data, respectively. Finally, the distribution of China's industrial SO<subscript>2</subscript> 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<subscript>2</subscript> emissions. The coefficient of determination (R<superscript>2</superscript>) 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<subscript>2</subscript> 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<subscript>2</subscript> 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<subscript>2</subscript> 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<subscript>2</subscript> emissions in China, but also offer some scientific references for formulating feasible industrial SO<subscript>2</subscript> emission reduction policies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 15
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 145674310
- Full Text :
- https://doi.org/10.1371/journal.pone.0238696