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Network Analysis of Fine Particulate Matter (PM 2.5 ) Emissions in China

View Article: PubMed Central - PubMed

ABSTRACT

Specification of PM2.5 spatial and temporal characteristics is important for understanding PM2.5 adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM2.5 emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM2.5 emissions from industrial sector 8 clusters were found in 2008 but they merged together into a huge cluster in 2010, suggesting that industrial sector underwent an integrating process. For PM2.5 emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM2.5 emissions from residential sector, the same pattern of 10 clusters was uncovered in both years, implicating the household energy consumption unchanged from 2008 to 2010. For PM2.5 emissions from transportation sector, the same pattern of 5 clusters with many connections in-between was unraveled, indicating the high-speed development of transportation nationalwidely. Except for the known elements, mercury (Hg) surfaced as an element for particle nucleation. To our knowledge, this is the first network study in this field.

No MeSH data available.


Network of PM2.5 emissions from transportation sector in China monitored by 1744 stations in 2010.
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f8: Network of PM2.5 emissions from transportation sector in China monitored by 1744 stations in 2010.

Mentions: Because of different sources in formation of aerosols, it necessarily analyzes PM2.5 pollution according to the emissions from industrial, electricity generation, residential and transportation sectors, from which the PM2.5 emissions were delineated in Figs 1–8 by means of the network analysis. In these figures, a symbol represents a monitoring station with its code, and 31 colors donate to 22 provinces, 4 municipalities and 5 autonomous regions in China. A line between two symbols interprets that PM2.5 emission profiles in the two monitoring stations have a good correlation. A cluster aggregates the symbols that more densely connect each other within the given cluster but sparsely connect with the symbols in other clusters. In accordance with specific features of PM2.5 emissions in China, a cluster can mainly come from the places in the same province, for example, the PM2.5 emissions from the sector of electricity generation in 2010 (Fig. 4). By contrast, a cluster can encompass several provinces, like the PM2.5 emissions from transportation sector in both 2008 and 2010 (Figs 7 and 8). Collectively, network analysis in Figs 1–8 not only throws new light into PM2.5 emission but also calls for new solutions in pollution control, policymaking, and environmental restoration.


Network Analysis of Fine Particulate Matter (PM 2.5 ) Emissions in China
Network of PM2.5 emissions from transportation sector in China monitored by 1744 stations in 2010.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5016853&req=5

f8: Network of PM2.5 emissions from transportation sector in China monitored by 1744 stations in 2010.
Mentions: Because of different sources in formation of aerosols, it necessarily analyzes PM2.5 pollution according to the emissions from industrial, electricity generation, residential and transportation sectors, from which the PM2.5 emissions were delineated in Figs 1–8 by means of the network analysis. In these figures, a symbol represents a monitoring station with its code, and 31 colors donate to 22 provinces, 4 municipalities and 5 autonomous regions in China. A line between two symbols interprets that PM2.5 emission profiles in the two monitoring stations have a good correlation. A cluster aggregates the symbols that more densely connect each other within the given cluster but sparsely connect with the symbols in other clusters. In accordance with specific features of PM2.5 emissions in China, a cluster can mainly come from the places in the same province, for example, the PM2.5 emissions from the sector of electricity generation in 2010 (Fig. 4). By contrast, a cluster can encompass several provinces, like the PM2.5 emissions from transportation sector in both 2008 and 2010 (Figs 7 and 8). Collectively, network analysis in Figs 1–8 not only throws new light into PM2.5 emission but also calls for new solutions in pollution control, policymaking, and environmental restoration.

View Article: PubMed Central - PubMed

ABSTRACT

Specification of PM2.5 spatial and temporal characteristics is important for understanding PM2.5 adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM2.5 emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM2.5 emissions from industrial sector 8 clusters were found in 2008 but they merged together into a huge cluster in 2010, suggesting that industrial sector underwent an integrating process. For PM2.5 emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM2.5 emissions from residential sector, the same pattern of 10 clusters was uncovered in both years, implicating the household energy consumption unchanged from 2008 to 2010. For PM2.5 emissions from transportation sector, the same pattern of 5 clusters with many connections in-between was unraveled, indicating the high-speed development of transportation nationalwidely. Except for the known elements, mercury (Hg) surfaced as an element for particle nucleation. To our knowledge, this is the first network study in this field.

No MeSH data available.