Dataset Information

Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China.

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.

SUBMITTER: Yan S

PROVIDER: S-EPMC5016853 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China.

Yan Shaomin S Wu Guang G

Scientific reports 20160909

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 proce ...[more]

PMID: 27608625

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Project description:The main objective of this study was chemical characterization and source apportionment of the oxidative potential of ambient PM2.5 samples collected in an urban background area in Athens, Greece. Ambient PM2.5 samples were collected during the summer (June-September) of 2017 and winter (February-March) of 2018 at a residential, urban background site in the outlying neighborhood of the Demokritos National Laboratory in Athens, Greece. The collected PM samples were analyzed for their chemical constituents including metals and trace elements, water-soluble organic carbon (WSOC), elemental and organic carbon (EC/OC), and marker of biomass burning (i.e., levoglucosan). In addition, the DCFH in vitro assay was performed to determine the oxidative potential of the PM2.5 samples. We performed a series of statistical analyses, including Spearman rank-order correlation analysis, principal component analysis (PCA), and multi linear regression (MLR) to determine the most significant species (as source tracers) contributing to the oxidative potential of PM2.5. Our findings revealed that the intrinsic (per PM mass) and extrinsic (per m3 of air volume) oxidative potentials of the collected ambient PM2.5 samples were significantly higher than those measured in many urban areas around the world. The results of the MLR analyses indicated that the major pollution sources contributing to the oxidative potential of ambient PM2.5 were vehicular emissions (characterized by EC) (44%), followed by secondary organic aerosol (SOA) formation (characterized by WSOC) (16%), and biomass burning (characterized by levoglucosan) (9%). The oxidative potential of the collected ambient PM2.5 samples was also higher in summer compared to the winter, mainly due to higher concentrations of EC and WSOC during this season. Results from this study corroborate the impact of traffic and SOA on the oxidative potential of ambient PM2.5 in greater Athens area, and can be helpful in adopting appropriate public health policies regarding detrimental outcomes of exposure to PM2.5.

Dataset Information

Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China.

Publications

Network Analysis of Fine Particulate Matter (PM2.5) Emissions in China.

Similar Datasets

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