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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ACP</journal-id>
<journal-title-group>
<journal-title>Atmospheric Chemistry and Physics</journal-title>
<abbrev-journal-title abbrev-type="publisher">ACP</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1680-7324</issn>
<publisher><publisher-name>Copernicus GmbH</publisher-name>
<publisher-loc>GÃ¶ttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/acp-11-2295-2011</article-id>
<title-group>
<article-title>Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Zhao</surname>
<given-names>Y.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Nielsen</surname>
<given-names>C. P.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Lei</surname>
<given-names>Y.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>McElroy</surname>
<given-names>M. B.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hao</surname>
<given-names>J.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>School of Environment, Tsinghua University, Beijing 100084, China</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing 100012, China</addr-line>
</aff>
<pub-date pub-type="epub">
<day>14</day>
<month>03</month>
<year>2011</year>
</pub-date>
<volume>11</volume>
<issue>5</issue>
<fpage>2295</fpage>
<lpage>2308</lpage>
<permissions>
<license xlink:type="simple">
<license-p>This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.atmos-chem-phys.net/11/2295/2011/acp-11-2295-2011.html">This article is available from http://www.atmos-chem-phys.net/11/2295/2011/acp-11-2295-2011.html</self-uri>
<self-uri xlink:href="http://www.atmos-chem-phys.net/11/2295/2011/acp-11-2295-2011.pdf">The full text article is available as a PDF file from http://www.atmos-chem-phys.net/11/2295/2011/acp-11-2295-2011.pdf</self-uri>
<abstract>
<p>The uncertainties of a national, bottom-up inventory of Chinese emissions of
anthropogenic SO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;x&lt;/sub&gt;, and particulate matter (PM) of different
size classes and carbonaceous species are comprehensively quantified, for
the first time, using Monte Carlo simulation. The inventory is structured by
seven dominant sectors: coal-fired electric power, cement, iron and steel,
other industry (boiler combustion), other industry (non-combustion
processes), transportation, and residential. For each parameter related to
emission factors or activity-level calculations, the uncertainties,
represented as probability distributions, are either statistically fitted
using results of domestic field tests or, when these are lacking, estimated
based on foreign or other domestic data. The uncertainties (i.e., 95%
confidence intervals around the central estimates) of Chinese emissions of
SO&lt;sub&gt;2&lt;/sub&gt;, NO&lt;sub&gt;x&lt;/sub&gt;, total PM, PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, black carbon (BC), and
organic carbon (OC) in 2005 are estimated to be &amp;minus;14%~13%,
&amp;minus;13%~37%, &amp;minus;11%~38%, &amp;minus;14%~45%,
&amp;minus;17%~54%, &amp;minus;25%~136%, and &amp;minus;40%~121%,
respectively. Variations at activity levels (e.g., energy consumption or
industrial production) are not the main source of emission uncertainties.
Due to narrow classification of source types, large sample sizes, and
relatively high data quality, the coal-fired power sector is estimated to
have the smallest emission uncertainties for all species except BC and OC.
Due to poorer source classifications and a wider range of estimated emission
factors, considerable uncertainties of NO&lt;sub&gt;x&lt;/sub&gt; and PM emissions from cement
production and boiler combustion in other industries are found. The
probability distributions of emission factors for biomass burning, the
largest source of BC and OC, are fitted based on very limited domestic field
measurements, and special caution should thus be taken interpreting these
emission uncertainties. Although Monte Carlo simulation yields narrowed
estimates of uncertainties compared to previous bottom-up emission studies,
the results are not always consistent with those derived from satellite
observations. The results thus represent an incremental research advance;
while the analysis provides current estimates of uncertainty to researchers
investigating Chinese and global atmospheric transport and chemistry, it
also identifies specific needs in data collection and analysis to improve on
them. Strengthened quantification of emissions of the included species and
other, closely associated ones â€“ notably CO&lt;sub&gt;2&lt;/sub&gt;, generated largely by the
same processes and thus subject to many of the same parameter uncertainties
â€“ is essential not only for science but for the design of policies to
redress critical atmospheric environmental hazards at local, regional, and
global scales.</p>
</abstract>
<counts><page-count count="14"/></counts>
</article-meta>
</front>
<body/>
<back>
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