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SO 2 Emissions in China – Their Network and Hierarchical Structures

View Article: PubMed Central - PubMed

ABSTRACT

SO2 emissions lead to various harmful effects on environment and human health. The SO2 emission in China has significant contribution to the global SO2 emission, so it is necessary to employ various methods to study SO2 emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO2 emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO2 emissions from power generation sector were highly individualized as small-sized clusters, the SO2 emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO2 emissions from residential sector was not impacted by time, and the SO2 emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO2 emissions from all four sectors and is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measure to combat SO2 emissions.

No MeSH data available.


Heatmap and hierarchical cluster analysis on SO2 emissions from power generation, industrial, residential and transportation sectors in 2010.Different colors in heatmap indicate the membership of SO2 emissions with respect to clusters in power generation, industrial, residential and transportation sectors (Figs. 1, 2, 3, 4, 5). The dendrograms on the top and left-hand side indicate hierarchical relationship. The labels are superimposed each other because 1744 monitoring stations are included in analysis (the hierarchical relationships of 1744 monitoring stations can be found in Table A7 in Supplementary information files).
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f6: Heatmap and hierarchical cluster analysis on SO2 emissions from power generation, industrial, residential and transportation sectors in 2010.Different colors in heatmap indicate the membership of SO2 emissions with respect to clusters in power generation, industrial, residential and transportation sectors (Figs. 1, 2, 3, 4, 5). The dendrograms on the top and left-hand side indicate hierarchical relationship. The labels are superimposed each other because 1744 monitoring stations are included in analysis (the hierarchical relationships of 1744 monitoring stations can be found in Table A7 in Supplementary information files).

Mentions: In order to get a balanced overview, Figure 6 puts all the SO2 emissions in terms of their cluster membership from all four sectors together with the use of heatmap and hierarchical cluster analysis in 2010. This hierarchical cluster analysis furthermore defines the patterns of SO2 emissions because network analysis can stratify SO2 emissions according to their similarity, but cannot define the hierarchical structure among clusters. On the right-hand side with respect to dendrogram structure on the left-hand side, we can see that the SO2 emissions from residential and transportation sectors are more similar, and then they merge with the SO2 emissions from industrial sector, and finally merge with the SO2 emissions from power generation sector. Clearly, the SO2 emission from power generation sector is different from others. Because 1744 monitoring stations are included in analysis, the labels are superimposed at the bottom of figure, but their hierarchical relationship is visible on the top of Fig. 6 (the hierarchical relationships of 1744 monitoring stations can be found in Table A7 in Supplementary information files). For example, an initial hierarchical relationship begins from merging of Dangshan (Anhui, 58015) and Funan (Anhui, 58202), and then Mianchi (Henan, 57063). For another example, Runan (Henan, 57197) merges with Xiaoxian (Anhui, 58016), and then merges with Bozhou (Anhui, 58102), which come from the merging of Guangshan (Henan, 57299) and Bozhou (Anhui, 58102) (Table A7 in Supplementary information files). Basically, this hierarchical structure is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measures to combat SO2 emissions.


SO 2 Emissions in China – Their Network and Hierarchical Structures
Heatmap and hierarchical cluster analysis on SO2 emissions from power generation, industrial, residential and transportation sectors in 2010.Different colors in heatmap indicate the membership of SO2 emissions with respect to clusters in power generation, industrial, residential and transportation sectors (Figs. 1, 2, 3, 4, 5). The dendrograms on the top and left-hand side indicate hierarchical relationship. The labels are superimposed each other because 1744 monitoring stations are included in analysis (the hierarchical relationships of 1744 monitoring stations can be found in Table A7 in Supplementary information files).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC5384192&req=5

f6: Heatmap and hierarchical cluster analysis on SO2 emissions from power generation, industrial, residential and transportation sectors in 2010.Different colors in heatmap indicate the membership of SO2 emissions with respect to clusters in power generation, industrial, residential and transportation sectors (Figs. 1, 2, 3, 4, 5). The dendrograms on the top and left-hand side indicate hierarchical relationship. The labels are superimposed each other because 1744 monitoring stations are included in analysis (the hierarchical relationships of 1744 monitoring stations can be found in Table A7 in Supplementary information files).
Mentions: In order to get a balanced overview, Figure 6 puts all the SO2 emissions in terms of their cluster membership from all four sectors together with the use of heatmap and hierarchical cluster analysis in 2010. This hierarchical cluster analysis furthermore defines the patterns of SO2 emissions because network analysis can stratify SO2 emissions according to their similarity, but cannot define the hierarchical structure among clusters. On the right-hand side with respect to dendrogram structure on the left-hand side, we can see that the SO2 emissions from residential and transportation sectors are more similar, and then they merge with the SO2 emissions from industrial sector, and finally merge with the SO2 emissions from power generation sector. Clearly, the SO2 emission from power generation sector is different from others. Because 1744 monitoring stations are included in analysis, the labels are superimposed at the bottom of figure, but their hierarchical relationship is visible on the top of Fig. 6 (the hierarchical relationships of 1744 monitoring stations can be found in Table A7 in Supplementary information files). For example, an initial hierarchical relationship begins from merging of Dangshan (Anhui, 58015) and Funan (Anhui, 58202), and then Mianchi (Henan, 57063). For another example, Runan (Henan, 57197) merges with Xiaoxian (Anhui, 58016), and then merges with Bozhou (Anhui, 58102), which come from the merging of Guangshan (Henan, 57299) and Bozhou (Anhui, 58102) (Table A7 in Supplementary information files). Basically, this hierarchical structure is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measures to combat SO2 emissions.

View Article: PubMed Central - PubMed

ABSTRACT

SO2 emissions lead to various harmful effects on environment and human health. The SO2 emission in China has significant contribution to the global SO2 emission, so it is necessary to employ various methods to study SO2 emissions in China with great details in order to lay the foundation for policymaking to improve environmental conditions in China. Network analysis is used to analyze the SO2 emissions from power generation, industrial, residential and transportation sectors in China for 2008 and 2010, which are recently available from 1744 ground surface monitoring stations. The results show that the SO2 emissions from power generation sector were highly individualized as small-sized clusters, the SO2 emissions from industrial sector underwent an integration process with a large cluster contained 1674 places covering all industrial areas in China, the SO2 emissions from residential sector was not impacted by time, and the SO2 emissions from transportation sector underwent significant integration. Hierarchical structure is obtained by further combining SO2 emissions from all four sectors and is potentially useful to find out similar patterns of SO2 emissions, which can provide information on understanding the mechanisms of SO2 pollution and on designing different environmental measure to combat SO2 emissions.

No MeSH data available.