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Article details
Title
Source apportionment of fine particulate matter organic carbon in Shenzhen, China by chemical mass balance and radiocarbon methods
Published in
Environmental Pollution, May 2018
DOI 10.1016/j.envpol.2018.04.071
Pubmed ID
Authors
Abstract

Chemical mass balance (CMB) modeling and radiocarbon measurements were combined to evaluate the sources of carbonaceous fine particulate matter (PM2.5) in Shenzhen, China during and after the 2011 summer Universiade games when air pollution control measurements were implemented to achieve air quality targets. Ambient PM2.5 filter samples were collected daily at two sampling sites (Peking University Shenzhen campus and Longgang) over 24 consecutive days, covering the controlled and uncontrolled periods. During the controlled period, the average PM2.5 concentration was less than half of what it was after the controls were lifted. Organic carbon (OC), organic molecular markers (e.g., levoglucosan, hopanes, polycyclic aromatic hydrocarbons), and secondary organic carbon (SOC) tracers were all significantly lower during the controlled period. After pollution controls ended, at Peking University, OC source contributions included gasoline and diesel engines (24%), coal combustion (6%), biomass burning (12.2%), vegetative detritus (2%), biogenic SOC (from isoprene, α-pinene, and β-caryophyllene; 7.1%), aromatic SOC (23%), and other sources not included in the model (25%). At Longgang after the controls ended, similar source contributions were observed: gasoline and diesel engines (23%), coal combustion (7%), biomass burning (17.7%), vegetative detritus (1%), biogenic SOC (from isoprene, α-pinene, and β-caryophyllene; 5.3%), aromatic SOC (13%), and other sources (33%). The contributions of the following sources were smaller during the pollution controls: biogenic SOC (by a factor of 10-16), aromatic SOC (4-12), coal combustion (1.5-6.8), and biomass burning (2.3-4.9). CMB model results and radiocarbon measurements both indicated that fossil carbon dominated over modern carbon, regardless of pollution controls. However, the CMB model needs further improvement to apportion contemporary carbon (i.e. biomass burning, biogenic SOC) in this region. This work defines the major contributors to carbonaceous PM2.5 in Shenzhen and demonstrates that control measures for primary emissions could significantly reduce secondary organic aerosol (SOA) formation.

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X Demographics

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 5 12%
Student > Ph. D. Student 4 10%
Researcher 3 7%
Lecturer > Senior Lecturer 2 5%
Other 2 5%
Other 4 10%
Unknown 21 51%
Readers by discipline
Readers by discipline Count As %
Environmental Science 8 20%
Nursing and Health Professions 2 5%
Agricultural and Biological Sciences 2 5%
Engineering 2 5%
Arts and Humanities 1 2%
Other 6 15%
Unknown 20 49%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 May 2018.
All research outputs
#22,767,715
of 25,382,440 outputs
Outputs from Environmental Pollution
#10,058
of 13,439 outputs
Outputs of similar age
#298,726
of 338,967 outputs
Outputs of similar age from Environmental Pollution
#223
of 296 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,439 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 338,967 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 296 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.