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Network Analysis of Fine Particulate Matter (PM(2.5)) Emissions in China
Specification of PM(2.5) spatial and temporal characteristics is important for understanding PM(2.5) adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM(2.5) emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The resul...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016853/ https://www.ncbi.nlm.nih.gov/pubmed/27608625 http://dx.doi.org/10.1038/srep33227 |
Sumario: | Specification of PM(2.5) spatial and temporal characteristics is important for understanding PM(2.5) adverse effects and policymaking. We applied network analysis to studying the dataset MIX, which contains PM(2.5) emissions recorded from 2168 monitoring stations in China in 2008 and 2010. The results showed that for PM(2.5) 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 PM(2.5) emissions from electricity generation sector, strong locality of clusters was revealed, implying that each region had its own electricity generation system. For PM(2.5) 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 PM(2.5) 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. |
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