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| Titel |
High-resolution mapping of vehicle emissions in China in 2008 |
| VerfasserIn |
B. Zheng, H. Huo, Q. Zhang, Z. L. Yao, X. T. Wang, X. F. Yang, H. Liu, K. B. He |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 18 ; Nr. 14, no. 18 (2014-09-17), S.9787-9805 |
| Datensatznummer |
250119041
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| Publikation (Nr.) |
 copernicus.org/acp-14-9787-2014.pdf |
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| Zusammenfassung |
| This study is the first in a series of papers that aim to develop
high-resolution emission databases for different anthropogenic sources in
China. Here we focus on on-road transportation. Because of the increasing
impact of on-road transportation on regional air quality, developing an
accurate and high-resolution vehicle emission inventory is important for
both the research community and air quality management. This work proposes a
new inventory methodology to improve the spatial and temporal accuracy and
resolution of vehicle emissions in China. We calculate, for the first time,
the monthly vehicle emissions for 2008 in 2364 counties (an administrative
unit one level lower than city) by developing a set of approaches to
estimate vehicle stock and monthly emission factors at county-level, and
technology distribution at provincial level. We then introduce allocation
weights for the vehicle kilometers traveled to assign the county-level
emissions onto 0.05° × 0.05° grids based on the
China Digital Road-network Map (CDRM). The new methodology overcomes the
common shortcomings of previous inventory methods, including neglecting the
geographical differences between key parameters and using surrogates that
are weakly related to vehicle activities to allocate vehicle emissions. The
new method has great advantages over previous methods in depicting the
spatial distribution characteristics of vehicle activities and emissions.
This work provides a better understanding of the spatial representation of
vehicle emissions in China and can benefit both air quality modeling and
management with improved spatial accuracy. |
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