The DOI matches its Crossref record.
Different characteristics of char and soot in the atmosphere and their ratio as an indicator for source identification in Xi'an, China
Open accessAbstract
Abstract. Numerous definitions and analytical techniques for elemental (or black) carbon (EC) have been published in the scientific literature, but still no generally accepted interdisciplinary definition exists. EC is not a single chemical compound, but is mainly composed of two parts of carbon contents: combustion residues from pyrolysis and combustion emissions formed via gas-to-particle conversion. Accordingly EC is subdivided into two classes: char and soot. Char is defined as carbonaceous materials obtained by heating organic substances and formed directly from pyrolysis, or as an impure form of graphitic carbon obtained as a residue when carbonaceous material is partially burned or heated with limited access of air. Soot is defined as only those carbon particles that form at high temperature via gas-phase processes. Since the different classes of EC have different chemical and physical properties, their optical light-absorbing properties differ, so that it is essential to differentiate them in the environment. The thermal optical reflectance (TOR) method was used to differentiate between char-EC and soot-EC according to its stepwise thermal evolutional oxidation of different carbon fractions under different temperatures and atmosphere. Char-EC and soot-EC are operationally defined as EC1-OP and EC2+EC3 (EC1, EC2 and EC3 corresponding to carbon fractions evolved at 550, 700 and 800 °C in a 98% He/2% O2 atmosphere, respectively), respectively. One year of observations of the daily and seasonal variations of carbonaceous particles were conducted in Xi'an, China in 2004 to demonstrate the different characteristics of char and soot in the atmosphere. Total carbon (TC), organic carbon (OC), EC and char-EC showed similar seasonal trends, with high concentrations in winter and low concentrations in summer, while soot-EC revealed relatively small seasonal variations, with maximum concentration (1.85±0.72 μg m−3) in spring and minimum concentration (1.15±0.47 μg m−3) in summer. The strong correlation between EC and char-EC (R2 = 0.99) and poor correlation between EC and soot-EC (R2 = 0.31) indicate that previously reported total EC in the literature reflected the distribution characteristics of char only, while overlooking that of soot. However, soot exhibits stronger light-absorbing characteristics than char, and merits greater focus in climate research. The small seasonal variation of soot-EC indicates that soot may be the background fraction in total EC, and is likely to have an even longer lifetime in the atmosphere than previously estimated for total EC, which suggests that soot may has a greater contribution to global warming. While both char-EC/soot-EC and primary OC/EC ratios vary with emission sources, only OC/EC ratio is affected by SOA. Thus char-EC/soot-EC may be a more effective indicator than OC/EC in source identification of carbonaceous aerosol. Comparison of seasonal variations of OC/EC and char-EC/soot-EC ratios in Xi'an confirms this point. However, wet scavenging by snow and rain was more effective for char than for soot and influenced the char-EC/soot-EC ratio, and this factor should be considered in source identification as well.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., et al. (2017) · Geoscientific Model Development · doi:10.5194/gmd-10-3329-2017 · Cited by 704
Kotchenruther, R. A. (2016) · Atmospheric Environment · doi:10.1016/j.atmosenv.2016.07.048 · Cited by 69
Emission characteristics of fine particulate matter from ultra-low emission power plants
Chen, X., Liu, Q., Yuan, C., Sheng, T., Zhang, X., Han, D., Xu, Z., Huang, X., Liao, H., Jiang, Y., et al. (2019) · Environmental Pollution · doi:10.1016/j.envpol.2019.113157 · Cited by 28
Metadata
- Authors
- Han, Y. M., Cao, J. J., Lee, S. C., Ho, K. F., & An, Z. S.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2010-01-22
- Volume
- 10
- Issue
- 2
- Pages
- 595-607
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2097729677
- Cited by
- 265
- References
- 0
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.