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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="3.0" xml:lang="en">
<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>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/acp-6-4287-2006</article-id>
<title-group>
<article-title>The sensitivity of aerosol in Europe to two different emission inventories and temporal distribution of emissions</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>de Meij</surname>
<given-names>A.</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>Krol</surname>
<given-names>M.</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>Dentener</surname>
<given-names>F.</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>Vignati</surname>
<given-names>E.</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>Cuvelier</surname>
<given-names>C.</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>Thunis</surname>
<given-names>P.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>now at: SRON, Utrecht, the Netherlands, and Wageningen University, The Netherlands</addr-line>
</aff>
<pub-date pub-type="epub">
<day>25</day>
<month>09</month>
<year>2006</year>
</pub-date>
<volume>6</volume>
<issue>12</issue>
<fpage>4287</fpage>
<lpage>4309</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>
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<abstract>
<p>The sensitivity to two different emission inventories, injection altitude
and temporal variations of anthropogenic emissions in aerosol modelling is
studied, using the two way nested global transport chemistry model TM5
focussing on Europe in June and December 2000. The simulations of gas and
aerosol concentrations and aerosol optical depth (AOD) with the EMEP and
AEROCOM emission inventories are compared with EMEP gas and aerosol surface
based measurements, AERONET sun photometers retrievals and MODIS satellite
data.

&lt;P&gt;

For the aerosol precursor gases SO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;x&lt;/sub&gt; in both months the
model results calculated with the EMEP inventory agree better (overestimated
by a factor 1.3 for both SO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;x&lt;/sub&gt;) with the EMEP measurements
than the simulation with the AEROCOM inventory (overestimated by a factor
2.4 and 1.9, respectively).

&lt;P&gt;

Besides the differences in total emissions between the two inventories, an
important role is also played by the vertical distribution of SO&lt;sub&gt;2&lt;/sub&gt; and
NO&lt;sub&gt;x&lt;/sub&gt; emissions in understanding the differences between the EMEP and
AEROCOM inventories.

&lt;P&gt;

In December NO&lt;sub&gt;x&lt;/sub&gt; and SO&lt;sub&gt;2&lt;/sub&gt; from both simulations agree within 50%
with observations.

&lt;P&gt;

In June SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;=&lt;/sup&gt; evaluated with the EMEP emission inventory agrees
slightly better with surface observations than the AEROCOM simulation,
whereas in December the use of both inventories results in an underestimate
of SO4 with a factor 2. Nitrate aerosol measured in summer is not reliable,
however in December nitrate aerosol calculations with the EMEP and AEROCOM
emissions agree with 30%, and 60%, respectively with the filter
measurements. Differences are caused by the total emissions and the temporal
distribution of the aerosol precursor gases NO&lt;sub&gt;x&lt;/sub&gt; and NH&lt;sub&gt;3&lt;/sub&gt;. Despite
these differences, we show that the column integrated AOD is less sensitive
to the underlying emission inventories. Calculated AOD values with both
emission inventories underestimate the observed AERONET AOD values by 20&amp;ndash;30%, whereas a case study using MODIS data shows a high spatial
agreement.

&lt;P&gt;

Our evaluation of the role of temporal distribution of anthropogenic
emissions on aerosol calculations shows that the daily and weekly temporal
distributions of the emissions are only important for NO&lt;sub&gt;x&lt;/sub&gt;, NH&lt;sub&gt;3&lt;/sub&gt; and
aerosol nitrate. However, for all aerosol species SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;=&lt;/sup&gt;,
NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, POM, BC, as well as for AOD, the seasonal temporal
variations used in the emission inventory are important. Our study shows the
value of including at least seasonal information on anthropogenic emissions,
although from a comparison with a range of measurements it is often
difficult to firmly identify the superiority of specific emission
inventories, since other modelling uncertainties, e.g.&amp;nbsp;related to transport,
aerosol removal, water uptake, and model resolution, play a dominant role.</p>
</abstract>
<counts><page-count count="23"/></counts>
</article-meta>
</front>
<body/>
<back>
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