the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Formation mechanism and source apportionment of water-soluble organic carbon in PM1, PM2.5 and PM10 in Beijing during haze episodes
Abstract. Water soluble organic carbon (WSOC) in atmospheric aerosols may pose significant impacts on haze formation, climate change, and human health. This study investigated the distribution characteristics and sources of WSOC in Beijing based on the diurnal PM1, PM2.5 and PM10 samples collected during haze episodes in winter and early spring of 2017. The haze episode in winter showed elevated level of WSOC, around three times of that in spring. WSOC was enriched in PM2.5 in winter while the proportions in both finer (0–1 μm) and coarse particles (2.5–10 μm) increased in spring. Several organic tracers were carefully selected and measured to demonstrate the sources and formation mechanism of WSOC. Most of the identified organic tracers showed similar seasonal variation, diurnal change and size distributions with WSOC, while the biogenic secondary organic aerosol (SOA) tracer cis-pinonic acid was an obvious exception. Based on the distribution characteristics of the secondary organic tracers and their correlation patterns with key influencing factors, the importance of the gas-phase versus aqueous-phase oxidation processes on SOA formation was explored. The gas-phase photochemical oxidation was weakened during haze episodes, whereas the aqueous-phase oxidation became the major pathway of SOA formation, especially in winter, at night and for the coarser particles. Secondary sources accounted for more than 50 % of WSOC in both winter and spring. Biomass burning was not the dominant source of WSOC in Beijing during haze episodes. Primary sources showed greater influence on finer particles while secondary sources became more important for coarser particles during haze episode in winter. SOC estimated by the OC-EC method, WSOC-levoglucosan method, and PMF-based methods were comparable, and the potential errors for different SOC estimation methods were discussed.
This preprint has been withdrawn.
-
Withdrawal notice
This preprint has been withdrawn.
-
Preprint
(1556 KB)
-
Supplement
(462 KB)
-
This preprint has been withdrawn.
- Preprint
(1556 KB) - Metadata XML
-
Supplement
(462 KB) - BibTeX
- EndNote
Interactive discussion
- RC1: 'Referee comments on ACP-2018-675', Anonymous Referee #2, 07 Oct 2018
- RC2: 'Comments on acp-2018-675', Anonymous Referee #1, 21 Oct 2018
- AC1: 'Author's responses to the reviewers’ comments on ACP-2018-675', Jing Chen, 10 Dec 2018
Interactive discussion
- RC1: 'Referee comments on ACP-2018-675', Anonymous Referee #2, 07 Oct 2018
- RC2: 'Comments on acp-2018-675', Anonymous Referee #1, 21 Oct 2018
- AC1: 'Author's responses to the reviewers’ comments on ACP-2018-675', Jing Chen, 10 Dec 2018
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
1,751 | 439 | 57 | 2,247 | 171 | 58 | 69 |
- HTML: 1,751
- PDF: 439
- XML: 57
- Total: 2,247
- Supplement: 171
- BibTeX: 58
- EndNote: 69
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
Qing Yu
Jing Chen
Weihua Qin
Yuepeng Zhang
Siming Cheng
Mushtaq Ahmad
Xingang Liu
Hezhong Tian
This preprint has been withdrawn.
- Preprint
(1556 KB) - Metadata XML
-
Supplement
(462 KB) - BibTeX
- EndNote