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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><abbrev-journal-title abbrev-type="nlm-ta">Atmos. Chem. Phys.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1680-7324</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/acp-20-2579-2020</article-id><title-group><article-title>Trends and source apportionment of aerosols<?xmltex \hack{\break}?> in Europe during 1980–2018</article-title><alt-title>Aerosol source apportionment in Europe</alt-title>
      </title-group><?xmltex \runningtitle{Aerosol source apportionment in Europe}?><?xmltex \runningauthor{Y. Yang et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Yang</surname><given-names>Yang</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-9008-5137</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff2">
          <name><surname>Lou</surname><given-names>Sijia</given-names></name>
          <email>lousijia@nju.edu.cn</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Wang</surname><given-names>Hailong</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1994-4402</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Pinya</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Liao</surname><given-names>Hong</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Jiangsu Key Laboratory of Atmospheric Environment Monitoring and
Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric
Environment and Equipment Technology, School of Environmental Science and
Engineering, <?xmltex \hack{\break}?> Nanjing University of Information Science and Technology,
Nanjing, Jiangsu, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu, China</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Atmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, Washington, USA</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Sijia Lou (lousijia@nju.edu.cn)</corresp></author-notes><pub-date><day>2</day><month>March</month><year>2020</year></pub-date>
      
      <volume>20</volume>
      <issue>4</issue>
      <fpage>2579</fpage><lpage>2590</lpage>
      <history>
        <date date-type="received"><day>30</day><month>August</month><year>2019</year></date>
           <date date-type="rev-request"><day>15</day><month>October</month><year>2019</year></date>
           <date date-type="rev-recd"><day>20</day><month>December</month><year>2019</year></date>
           <date date-type="accepted"><day>4</day><month>February</month><year>2020</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2020 </copyright-statement>
        <copyright-year>2020</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://acp.copernicus.org/articles/.html">This article is available from https://acp.copernicus.org/articles/.html</self-uri><self-uri xlink:href="https://acp.copernicus.org/articles/.pdf">The full text article is available as a PDF file from https://acp.copernicus.org/articles/.pdf</self-uri>
      <abstract><title>Abstract</title>
    <p id="d1e134">Aerosols have significantly affected health, environment, and climate in
Europe. Aerosol concentrations have been declining since the 1980s in Europe,
mainly owing to a reduction of local aerosol and precursor emissions.
Emissions from other source regions of the world, which have been changing
rapidly as well, may also perturb the historical and future trends of
aerosols and change their radiative impact in Europe. This study examines
trends of aerosols in Europe during 1980–2018 and quantifies contributions
from 16 source regions using the Community Atmosphere Model version 5
with Explicit Aerosol Source Tagging (CAM5-EAST). The simulated
near-surface total mass concentration of sulfate, black carbon, and primary
organic carbon had a 62 % decrease during 1980–2018. The majority of which was contributed to reductions of local emissions in Europe, and
8 %–9 % was induced by a decrease in emissions from
Russia–Belarus–Ukraine. With the decreases in the fractional contribution of
local emissions, aerosols transported from other source regions are
increasingly important for air quality in Europe. During 1980–2018, the
decrease in sulfate loading led to a warming effect of 2.0 W m<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in
Europe, with 12 % coming from changes in non-European sources, especially
from North America and Russia–Belarus–Ukraine. According to the Shared
Socioeconomic Pathways (SSP) scenarios, contributions to the sulfate
radiative forcing over Europe from both local European emissions and
non-European emissions should decrease at a comparable rate in the next 3
decades, suggesting that future changes in non-European emissions are as
important as European emissions for causing possible regional climate change
associated with aerosols in Europe.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e158">Aerosols are the main air pollutants that contribute to excess morbidity and
premature mortality by damaging cardiovascular and respiratory systems
(Lelieveld et al., 2019). They also have adverse effects on atmospheric
visibility for road and air traffic (Vautard et al., 2009). During the 1952
London Great Smog, air pollution associated with extremely high level of aerosols
caused thousands of premature deaths (Bell and Davis, 2001), which resulted
in a number of air quality legislative changes for reducing air pollution in Europe
(Brimblecombe, 2006).</p>
      <p id="d1e161">Besides the health and environment effects, aerosols can significantly
impact regional and global climate by perturbing the Earth's radiation
fluxes and influencing cloud microphysics (Boucher et al., 2013). Globally,
anthropogenic aerosols exert a net cooling effect in the Earth system and
have dampened the warming driven by greenhouse gases since the
preindustrial era. Due to a strong surface albedo feedback over polar
regions, the per unit aerosol emission from western Europe was reported to have
a greater cooling effect than any other major source regions of the globe
(Persad and Caldeira, 2018), revealing the importance of understanding
aerosol variations in Europe.</p>
      <?pagebreak page2580?><p id="d1e164">Significant reductions in near-surface aerosol concentrations and aerosol
optical depth (AOD) have been observed in Europe during the last few decades
from long-term station measurements and satellite retrievals (de Meij et
al., 2012; Tørseth et al., 2012). The decrease in aerosols has been
considered as a cause of the increase in surface solar radiation over Europe
since the 1980s (Wild, 2009), as well as a contributor to the eastern
European warming (Vautard et al., 2009), Arctic amplification (Acosta
Navarro et al., 2016), and increased atmospheric visibility over Europe
(Stjern et al., 2011) during the past 3 decades.</p>
      <p id="d1e167">The decrease in aerosols over Europe was mainly attributed to continuous
reductions in local European anthropogenic emissions of aerosols and
precursor gases since the 1980s (Smith et al., 2011), which are a result of
legislation for improving air quality. In addition to local emissions,
aerosol levels can also be affected by aerosol transport at continental
scales (Zhang et al., 2017; Yang et al., 2018a). Aerosol emissions in major
economic regions of the world have been changing rapidly during the past few
decades owing to economic development and environmental measures. North
America has started reducing emissions since the 1980s, and emissions in
Russia also showed decreasing trends after the dissolution of the Soviet
Union (Smith et al., 2011). In the meantime, aerosol emissions from East
Asia and South Asia have largely increased due to economic growth, although
emissions in China have been undergoing a remarkable reduction in recent years as a result of strict air quality regulations (Streets et al.,
2000; Li et al., 2017). It is important to understand the relative roles of
local emissions and regional transport in affecting long-term variations in
aerosols in Europe from both air quality and climate perspectives.</p>
      <p id="d1e171">Source apportionment is useful for quantifying contributions to aerosols
from specific source regions and/or sectors and is beneficial to the
emission control strategies. The traditional method of examining the
source–receptor relationship in aerosol models is to zero out or perturb a
certain percent of emissions from a given source region or sector in
parallel sensitivity simulations along with a baseline simulation, and it has
been used in many studies to examine source contributions of particulate
matter (PM) in Europe from different sectors (e.g., Sartelet et al., 2012;
Tagaris et al., 2015; Aksoyoglu et al., 2016). Recently, source region
contributions to European CO and <inline-formula><mml:math id="M2" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> levels, as well as global and
regional aerosol radiative forcing, were examined under the Hemispheric
Transport of Air Pollution model experiment phase 2 (HTAP2) protocol, in
which sensitivity simulations were conducted with reductions in anthropogenic emissions
from different source regions by 20 % (Stjern et al., 2016; Jonson et al., 2018). This
method suffers from a large computational cost for the excessive model
simulations when estimating contributions from a large number of sources,
and contributions from all sources do not sum up to 100 % of the total
concentration in the default simulation (Koo et al., 2009; Wang et al., 2014).</p>
      <p id="d1e185">The explicit aerosol tagging method, which simultaneously tracks
contributions from many different sources, is a useful tool for assessing the
source–receptor relationship of aerosols. This method has previously been
adopted in regional air quality models such as CAMx (the Comprehensive Air
quality Model with Extensions) and CMAQ (the Community Multiscale Air
Quality model). Using regional air quality models with aerosol tagging,
contributions from different source sectors and local/regional sources to
European PM and its health impact were studied (Brandt et al., 2013;
Skyllakou et al., 2014; Karamchandani et al., 2017). However, due to the
limitation in domain size of regional air quality models, contributions of
intercontinental transport from sources outside the domain are difficult to
account for.</p>
      <p id="d1e188">Anthropogenic emissions of aerosols and their precursor gases from different
economic regions of the world have changed substantially during the past few
decades. Very few studies have examined the source apportionment of aerosols
in Europe coming from sources all over the changing world. In this study, source
attributions of concentrations, column burden, optical depth of aerosols in
four major areas of Europe from 16 source regions of the globe over
1980–2018 are quantified. This is facilitated by the explicit aerosol
source tagging technique that was recently implemented in a global
aerosol–climate model (CAM5-EAST; Community Atmosphere
Model version 5 with Explicit Aerosol Source Tagging). This technique has lately been used to
examine the source attribution of aerosol trends in China and the United States during
1980–2014 (Yang et al., 2018a, b). The source apportionment analysis is
extended to the year 2018 using the Shared Socioeconomic Pathways (SSPs)
scenario, with a focus on Europe here.</p>
      <p id="d1e191">The CAM5-EAST model, along with the aerosol source tagging technique, and
aerosol emissions are described in Sect. 2. Section 3 evaluates the model
performance in simulating aerosols in Europe. Section 4 shows the analysis of
source–receptor relationships of aerosols in Europe on a climatological average.
Source contributions to long-term variations in European aerosols and their
direct radiative forcing (DRF) during 1980–2018, as well as future forcing
prediction, are provided in Sect. 5. Section 6 summarizes these results and
conclusions.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Model description and experimental setup</title>
      <?pagebreak page2581?><p id="d1e209">The global aerosol–climate model CAM5 (Community Atmosphere Model version 5), which was developed as the atmospheric component of CESM (the Community
Earth System Model; Hurrell et al., 2013), is applied to simulate aerosols
at a spatial resolution of 1.9<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> latitude <inline-formula><mml:math id="M4" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 2.5<inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>
longitude, and 30 vertical layers from the surface to 3.6 hPa. Aerosol
species, including sulfate, black carbon (BC), primary organic aerosol
(POA), second organic aerosol (SOA), mineral dust, and sea salt, can be
simulated in a modal aerosol module of CAM5. The three-mode aerosol module
(MAM3) configuration is chosen with consideration for the computational
efficiency of long-term simulation. Details of the MAM3 aerosol
representation in CAM5 are described in Liu et al. (2012). On top of the
default CAM5, some aerosol-related scheme modifications are utilized to
improve the model performance for aerosol convective transport and wet
deposition (Wang et al., 2013).</p>
      <p id="d1e237">A 40-year (1979–2018), historical Atmospheric Model
Intercomparison Project (AMIP)-type simulation has been performed, following CMIP6 (the Coupled Model Intercomparison Project Phase 6) configurations and forcing
conditions. Time-varying sea surface temperatures, sea ice concentrations,
solar insolation, greenhouse gas concentrations, and aerosol emissions are
prescribed in the simulation. To better reproduce large-scale circulation
patterns for aerosol transport in the model, wind fields are nudged with the
MERRA-2 (Modern-Era Retrospective analysis for Research and Applications,
Version 2) reanalysis (Gelaro et al., 2017).</p>
      <p id="d1e240">Aerosol DRF is defined in this study as the difference in clear-sky
radiative fluxes at the top of the atmosphere between two diagnostic
calculations in the radiative transfer scheme, with and without specific
aerosol species accounted for, respectively. Historical variations in sulfate DRF
due to anthropogenic emissions from Europe and outside Europe are quantified
in this study. Rather than sulfate, DRF of other aerosol species is not
calculated in this study due to the computational limitation considering
multiple source regions. However, because sulfate dominates the decrease in
total combustion AOD in Europe, shown below, the sulfate DRF is calculated to
roughly represent the DRF caused by the total combustion AOD change. Future
DRF of sulfate aerosols over Europe is also estimated by scaling the
historical mean (1980–2018) sulfate DRF by the ratio of SSPs future
<inline-formula><mml:math id="M6" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emissions (Riahi et al., 2017) to historical emissions and assuming a linear response of DRF to AOD and regional emissions. This DRF prediction method has been used to
estimate the East Asian contribution to sulfate DRF in the United States in the 2030s (Yang
et al., 2018a).</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Aerosol source tagging technique</title>
      <p id="d1e262">The Explicit Aerosol Source Tagging (EAST) technique, which was recently
implemented in CAM5 (Wang et al., 2014; Yang et al., 2017a, b), is used to
examine the long-term source apportionment of aerosols in Europe. Unlike the
traditional back-trajectory and emission perturbation methods, EAST has the
identical physical, chemical, and dynamical processes considered
independently for aerosol species (defined as new tracers) emitted from each
of the tagged source regions and/or sectors in the simulation. Sulfate, BC,
POA, and SOA from predefined sources can be explicitly tracked, from
emission to deposition, in one CAM5-EAST simulation. Due to the
computational constraint and potentially large model bias from the
simplified SOA treatment (Yang et al., 2018a), we focus on
sulfate, BC, and POA in this study but quantify the potential impact of SOA
on the aerosol variation.</p>
      <p id="d1e265">The global aerosol and precursor emissions are decomposed into 16
source regions defined in the HTAP2 protocol, including Europe (EUR), North
America (NAM), Central America (CAM), South America (SAM), northern Africa
(NAF), southern Africa (SAF), the Middle East (MDE), southeast Asia (SEA),
central Asia (CAS), South Asia (SAS), East Asia (EAS),
Russia–Belarus–Ukraine (RBU), Pacific–Australia–New Zealand (PAN), the
Arctic (ARC), Antarctic (ANT), and non-Arctic/Antarctic ocean (OCN) (Fig. 1). Note that sources from marine and volcanic eruptions are included in
OCN. The focused receptor region in this study is Europe, which is further
divided into northwestern Europe (NWE or NW Europe), southwestern Europe
(SWE or SW Europe), eastern Europe (EAE or E Europe), and
Greece–Turkey–Cyprus (GTC) based on the finer source region selection in HTAP2.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><label>Figure 1</label><caption><p id="d1e270">Source regions that are selected for the Explicit Aerosol
Source Tagging (EAST) in this study, including Europe (EUR), North America
(NAM), Central America (CAM), South America (SAM), northern Africa (NAF), southern
Africa (SAF), the Middle East (MDE), southeast Asia (SEA), central Asia
(CAS), South Asia (SAS), East Asia (EAS), Russia–Belarus–Ukraine (RBU),
Pacific–Australia–New Zealand (PAN), the Arctic (ARC), Antarctic (ANT), and
non-Arctic/Antarctic ocean (OCN). The embedded panel (at bottom left) is
Europe, as the receptor region, which is further divided to northwestern
Europe (NWE), southwestern Europe (SWE), eastern Europe (EAE), and
Greece–Turkey–Cyprus (GTC).</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f01.png"/>

        </fig>

</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Aerosol and precursor emissions</title>
      <p id="d1e287">Following the CMIP6-AMIP protocol, historical anthropogenic (Hoesly et al.,
2018) and biomass burning (van Marle et al., 2017) emissions of aerosol and
precursor gases are used for 1979–2014. For the remaining 4 years
(2015–2018), emissions are interpolated from the SSP2-4.5 forcing scenario,
in which aerosol pathways are not as extreme as other SSPs and have been
used in many model intercomparison projects for CMIP6 (O'Neill et al.,
2016). Figure S1 in the Supplement shows the spatial distribution and time series of
anthropogenic emissions of <inline-formula><mml:math id="M7" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> (precursor gas of sulfate aerosol), BC,
and POA from Europe for 1980–2018. High emissions are located over E
Europe and NW Europe, from which the emissions of <inline-formula><mml:math id="M8" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, BC, and POA were
reduced by 84 %–93 %, 43 %–62 %, and 28 %–36 %, respectively, in
2014–2018 relative to 1980–1984. Although SW Europe had a relatively low
total amount of emissions compared to E Europe and NW Europe, it had
significant reductions in <inline-formula><mml:math id="M9" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and BC emissions, 91 % and 55 %,
respectively. Over the GTC region, <inline-formula><mml:math id="M10" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and BC emissions were increased by
49 % and 48 %, respectively. Considering the subregions as a whole,
<inline-formula><mml:math id="M11" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, BC, and POA emissions from Europe have decreased by 12.57 Tg yr<inline-formula><mml:math id="M12" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (83 %), 0.22 Tg yr<inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (46 %), and 0.30 Tg yr<inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>
(24 %) in 2014–2018 compared to 1980–1984 (Table 1). Historical changes
in emissions from other source regions can be found in Hoesly et al. (2018)
and Yang et al. (2018b).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e385">Annual emission (in teragrams per year), concentration
(in micrograms per cubic meter), column burden (in milligrams per square meter), AOD (scaled up by a factor
of 100), and DRF (in watts per square meter) of sulfate, BC, POA, SBP
(sulfate-BC-POA), and SBP-SOA (sulfate-BC-POA-SOA) in Europe averaged over
1980–1984 and 2014–2018, as well as the differences between 1980–1984 and
2014–2018. Differences in percentage relative to mean values in 1980–1984
are presented in parentheses.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3">Emissions</oasis:entry>
         <oasis:entry colname="col4">Concentrations</oasis:entry>
         <oasis:entry colname="col5">Burden</oasis:entry>
         <oasis:entry colname="col6">AOD<inline-formula><mml:math id="M15" display="inline"><mml:mrow><mml:mo>×</mml:mo><mml:mn mathvariant="normal">100</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col7">DRF</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">1980–1984</oasis:entry>
         <oasis:entry colname="col3">15.10</oasis:entry>
         <oasis:entry colname="col4">6.00</oasis:entry>
         <oasis:entry colname="col5">14.35</oasis:entry>
         <oasis:entry colname="col6">9.13</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3.27</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Sulfate</oasis:entry>
         <oasis:entry colname="col2">2014–2018</oasis:entry>
         <oasis:entry colname="col3">2.53</oasis:entry>
         <oasis:entry colname="col4">1.80</oasis:entry>
         <oasis:entry colname="col5">5.79</oasis:entry>
         <oasis:entry colname="col6">3.24</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.24</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M18" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M19" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">12.57</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M20" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">83.2</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M21" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4.20</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">70.0</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M23" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">8.55</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M24" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">59.6</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col6"><inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">5.89</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">64.6</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col7">2.04 (<inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">62.2</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">1980–1984</oasis:entry>
         <oasis:entry colname="col3">0.47</oasis:entry>
         <oasis:entry colname="col4">0.4</oasis:entry>
         <oasis:entry colname="col5">0.38</oasis:entry>
         <oasis:entry colname="col6">0.7</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">BC</oasis:entry>
         <oasis:entry colname="col2">2014–2018</oasis:entry>
         <oasis:entry colname="col3">0.25</oasis:entry>
         <oasis:entry colname="col4">0.23</oasis:entry>
         <oasis:entry colname="col5">0.28</oasis:entry>
         <oasis:entry colname="col6">0.5</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M28" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.22</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M30" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">45.8</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M31" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.17</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M32" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">43.0</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.11</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M34" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">27.6</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col6"><inline-formula><mml:math id="M35" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.21</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">29.2</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">1980–1984</oasis:entry>
         <oasis:entry colname="col3">1.24</oasis:entry>
         <oasis:entry colname="col4">1.12</oasis:entry>
         <oasis:entry colname="col5">1.12</oasis:entry>
         <oasis:entry colname="col6">0.63</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">POA</oasis:entry>
         <oasis:entry colname="col2">2014–2018</oasis:entry>
         <oasis:entry colname="col3">0.94</oasis:entry>
         <oasis:entry colname="col4">0.86</oasis:entry>
         <oasis:entry colname="col5">1.08</oasis:entry>
         <oasis:entry colname="col6">0.58</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M37" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M38" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.30</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M39" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">24.4</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M40" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.26</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M41" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">23.2</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M42" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.04</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3.8</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col6"><inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M45" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">7.5</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">1980–1984</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4">7.52</oasis:entry>
         <oasis:entry colname="col5">15.85</oasis:entry>
         <oasis:entry colname="col6">10.46</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Sulfate-BC-POA</oasis:entry>
         <oasis:entry colname="col2">2014–2018</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4">2.89</oasis:entry>
         <oasis:entry colname="col5">7.15</oasis:entry>
         <oasis:entry colname="col6">4.32</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M46" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M47" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4.63</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M48" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">61.6</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">8.70</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">54.9</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col6"><inline-formula><mml:math id="M51" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">6.15</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">58.7</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">1980–1984</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4">10.48</oasis:entry>
         <oasis:entry colname="col5">19.58</oasis:entry>
         <oasis:entry colname="col6">11.92</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">SBP-SOA</oasis:entry>
         <oasis:entry colname="col2">2014–2018</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4">4.34</oasis:entry>
         <oasis:entry colname="col5">8.55</oasis:entry>
         <oasis:entry colname="col6">5.44</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"><inline-formula><mml:math id="M53" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M54" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">6.14</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">58.6</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col5"><inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">11.03</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">56.3</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col6"><inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">6.48</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M59" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">54.37</mml:mn></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col7">–</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

<?xmltex \hack{\newpage}?>
</sec>
</sec>
<?pagebreak page2582?><sec id="Ch1.S3">
  <label>3</label><title>Model evaluation</title>
      <p id="d1e1217">EMEP (European Monitoring and Evaluation Programme, <uri>http://www.emep.int</uri>, last access: 27 February 2020)
networks provide daily near-surface aerosol concentrations in Europe. The
annual mean of daily observations is used to evaluate the model performance
in this study. Compared to the observational data from EMEP networks during
2010–2014, CAM5-EAST can reproduce well the spatial distribution and
magnitude of aerosol components with normalized mean biases (NMBs) of
<inline-formula><mml:math id="M60" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">14</mml:mn></mml:mrow></mml:math></inline-formula> % to <inline-formula><mml:math id="M61" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">23</mml:mn></mml:mrow></mml:math></inline-formula> % and correlation coefficients (<inline-formula><mml:math id="M62" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula>) in a range
of 0.43–0.62 for sulfate, BC, and organic carbon (OC; derived
from POA and SOA from the model results) (Fig. 2).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><?xmltex \currentcnt{2}?><label>Figure 2</label><caption><p id="d1e1252">Spatial distribution of simulated (contour) and observed
(color-filled circles) annual mean near-surface <bold>(a)</bold> sulfate, <bold>(b)</bold> BC, and <bold>(c)</bold> OC – derived as (POA <inline-formula><mml:math id="M63" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> SOA)/1.4 in model – concentrations (in micrograms per cubic meter)
for 2010–2014. Observations are from EMEP (European Monitoring and
Evaluation Programme) networks. Normalized mean bias, <inline-formula><mml:math id="M64" display="inline"><mml:mrow><mml:mi mathvariant="normal">NMB</mml:mi><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mo>=</mml:mo><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mn mathvariant="normal">100</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="italic">%</mml:mi><mml:mo>×</mml:mo><mml:mo>∑</mml:mo><mml:mfenced close=")" open="("><mml:mrow><mml:msub><mml:mi mathvariant="normal">Model</mml:mi><mml:mi mathvariant="normal">site</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi mathvariant="normal">Observation</mml:mi><mml:mi mathvariant="normal">site</mml:mi></mml:msub></mml:mrow></mml:mfenced><mml:mo>/</mml:mo><mml:mo>∑</mml:mo><mml:msub><mml:mi mathvariant="normal">Observation</mml:mi><mml:mi mathvariant="normal">site</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, and correlation coefficient (<inline-formula><mml:math id="M65" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula>) between observed and
simulated concentrations are noted at the top of each panel.</p></caption>
        <?xmltex \igopts{width=142.26378pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f02.png"/>

      </fig>

      <p id="d1e1330">Figure 3 shows the time series of annual mean near-surface sulfate, BC, and
OC concentrations averaged over EMEP sites in Europe and the corresponding
model values during 1993–2018. Variations in near-surface sulfate
concentrations are consistent between the model and observations, with <inline-formula><mml:math id="M66" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula>
values higher than 0.9. The observed variations in BC and OC concentrations
in Europe are represented in the simulation, with <inline-formula><mml:math id="M67" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula> values of 0.52 and 0.65,
respectively. However, the observed high values of BC and OC concentrations
are not captured by the model, probably because very few<?pagebreak page2583?> data were available
before 2010, and, therefore, any difference between model and observation
cannot be smoothed out through the spatial average. This is also indicated
by the large spatial variation in BC and OC concentrations before 2010.
Nevertheless, the modeled concentrations are still within the range of
observations. Note that the number of sites used for the spatial average in
Fig. 3 is different from year to year, and thus the variation or trend here
does not represent that over a subregion or the entire Europe.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><?xmltex \currentcnt{3}?><label>Figure 3</label><caption><p id="d1e1350">Time series (1993–2018) of spatial and annual mean
near-surface <bold>(a)</bold> sulfate, <bold>(b)</bold> BC, and <bold>(c)</bold> OC concentrations (in micrograms per cubic meter) in Europe from model simulation (blue lines) and observations (red
lines). Model results are plotted only when EMEP observational data are
available. Shaded areas represent 1<inline-formula><mml:math id="M68" display="inline"><mml:mi mathvariant="italic">σ</mml:mi></mml:math></inline-formula> spatial standard deviation of
annual mean concentrations for each year. Temporal correlation coefficients
(<inline-formula><mml:math id="M69" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula>) between observed and simulated spatially averaged concentrations are
noted in the top-right corner of each panel.</p></caption>
        <?xmltex \igopts{width=142.26378pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f03.png"/>

      </fig>

      <p id="d1e1382">The modeled AOD is evaluated against the AERONET (Aerosol Robotic Network;
<uri>https://aeronet.gsfc.nasa.gov</uri>, last access: 27 February 2020) data in Fig. 8. Both the modeled and
observed AOD show decreasing trends during 2001–2018. The model
underestimates AOD in all four subregions of Europe, probably due to the
lack of nitrate aerosol. The variations in AOD in western Europe (combined
NW and SW Europe) are well predicted, with <inline-formula><mml:math id="M70" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula> values of about 0.75, but the
model barely reproduces the AOD variations in E Europe and the GTC region
(<inline-formula><mml:math id="M71" display="inline"><mml:mrow><mml:mi>R</mml:mi><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">0.5</mml:mn></mml:mrow></mml:math></inline-formula>). The difference in the interannual variation in AOD between
the model simulation and observation can be caused by many factors such as
aerosol emissions, aerosol parameterizations in the model, the aerosol mixing state,
inaccurate meteorological fields from reanalysis data, and biases in
measurements. However, identifying the contribution of each factor to the
difference is beyond the scope of this paper.</p>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Source apportionment of aerosols in Europe</title>
      <p id="d1e1415">Based on the tagging technique in CAM5-EAST, near-surface concentrations of
total sulfate-BC-POA can be attributed to emissions within and outside
Europe, as shown in Fig. 4a and b, and the relative contributions in
percentage<?pagebreak page2584?> are given in Fig. 4c and d. Averaged over 2010–2018, due to
the relatively high local emissions, annual mean sulfate-BC-POA
concentrations contributed to by European emissions show peak values of 4 <inline-formula><mml:math id="M72" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>g m<inline-formula><mml:math id="M73" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in E Europe. The slight increase in <inline-formula><mml:math id="M74" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emission from the
GTC region (Fig. S1), which is opposite to the decreases in the other
three subregions of Europe, also leads to high concentrations in GTC, with
2–4 <inline-formula><mml:math id="M75" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>g m<inline-formula><mml:math id="M76" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> contributed by European emissions. Due to the
atmospheric transport from surrounding regions including northern Africa, the
Middle East, and Russia–Belarus–Ukraine, non-European emissions account for
0.5–1 <inline-formula><mml:math id="M77" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>g m<inline-formula><mml:math id="M78" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> over SW Europe, E Europe, and the GTC area. Overall,
local European emissions are the dominant sources of sulfate-BC-POA
near-surface concentrations in Europe, with contributions larger than 80 %
(60 %) in central areas (most of Europe). Non-European emissions are
responsible for 30 %–50 % of the near-surface concentrations near the
coastal areas and boundaries of the Europe that are easily influenced by
aerosol regional transport.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><?xmltex \currentcnt{4}?><label>Figure 4</label><caption><p id="d1e1492"><bold>(a, b)</bold> Absolute (in micrograms per cubic) and <bold>(c, d)</bold> relative contributions (in percent) to annual mean near-surface concentrations of sulfate-BC-POA from local European emissions (EUR) and emissions outside the Europe (Non-EUR), respectively, averaged over 2010–2018.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f04.png"/>

      </fig>

      <p id="d1e1506">Figure 5 illustrates the source contributions in percentage of emissions
from different regions of the globe to near-surface aerosol concentrations
and column burdens over the four subregions of Europe averaged over
2010–2018. Different aerosols have fairly different local/remote source
attributions depending on the local to remote emission ratio and transport
efficiency. European emissions explain 54 %–68 % of near-surface
sulfate concentrations over the four subregions of Europe, with the largest
local contribution in E Europe due to the relatively high emission rate.
The emissions from Europe dominate BC and POA concentrations in Europe, with
contributions in the range of 78 %–95 % and 58 %–78 %,
respectively. The impact of local emissions on near-surface sulfate
concentration is smaller than BC and POA. This is partially due to its less-efficient gas scavenging than the particles and the additional
<inline-formula><mml:math id="M79" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>-to-sulfate conversion process that increases the atmospheric
residence time of sulfur. On the other hand, the higher initial injection
height of <inline-formula><mml:math id="M80" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emissions from some sources (e.g., industrial sector and
power plants) facilitates the lifting of <inline-formula><mml:math id="M81" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> and sulfate aerosol into
the free atmosphere and, therefore, favors long-range transport (Yang et
al., 2019). The efficient reduction of local <inline-formula><mml:math id="M82" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emissions in Europe
also caused lower influences of local emissions on sulfate
concentrations in recent years.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><?xmltex \currentcnt{5}?><label>Figure 5</label><caption><p id="d1e1556">Relative contributions (in percent) by emissions from major
tagged source regions including Europe (EUR), North America (NAM), northern
Africa (NAF), the Middle East (MDE), East Asia (EAS), Russia–Belarus–Ukraine
(RBU), non-Arctic/Antarctic ocean (OCN), and other (OTH) regions to
near-surface concentrations <bold>(a, b, c)</bold> and column burdens <bold>(d, e, f)</bold> of sulfate, BC,
and POA (from top to bottom) in the four subregions of Europe, averaged over
2010–2018. Patterned areas represent local EUR contributions.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f05.png"/>

      </fig>

      <p id="d1e1571">Anthropogenic emissions over oceans (e.g., international shipping) and
natural emissions of oceanic dimethyl sulfide (DMS) and volcanic activities
together account for 16 %–28 % of near-surface sulfate concentrations
in the four subregions of Europe. About 10 % of sulfate and 5 %–10 %
of BC and POA in E Europe and GTC come from Russia–Belarus–Ukraine
emissions. Northern Africa contributes to 7 % of sulfate, 17 % of BC, and
24 % of POA over SW Europe. The contribution of emissions from the
Middle East to aerosol concentrations in GTC are between 5 % and 10 %.</p>
      <p id="d1e1574">The transboundary and intercontinental transports of aerosols occur most
frequently in the free troposphere rather than near the surface (Figs. S2
and S3). This also leads to larger relative contributions from non-European
sources to aerosol column burdens than to the near-surface concentrations
(Fig. 5). The European emissions only contribute 32 %–47 % of column
burden of sulfate, 57 %–75 % of BC, and 51 %–71 % of POA over the
four subregions of Europe. Over NW Europe and SW Europe, about
10 %–15 % of the<?pagebreak page2585?> sulfate burden is from East Asia and
Russia–Belarus–Ukraine, respectively. Sources in northern Africa are
responsible for 27 % and 14 % of BC and 19 % and 11 % of POA burden
over SW Europe and GTC, respectively. Emissions from North America account
for 15 % and 10 % of POA burden over NW Europe and SW Europe, respectively.
Emissions from Russia–Belarus–Ukraine explain 12 % and 19 % of POA
burden over E Europe and GTC, respectively. Since near-surface aerosol
concentrations directly affect air quality and column burden is more
relevant to radiative impact, the differences in relative contributions
between near-surface concentrations and column burden highlight the possible
roles of nonlocal emissions in either air quality or energy balance over
Europe.</p>
      <p id="d1e1577">Source contributions to aerosols in Europe vary with season due to the
seasonality of emissions and meteorology. In general, local sources have the
largest contributions to both near-surface concentration and column burden
of European aerosols in winter and the smallest contributions in summer (averaged
over 2010–2018; outer rings in Fig. 6). With the contributions normalized
by the ratio of the seasonal anthropogenic emission to the annual mean for each
source, the impact of the seasonal variation in emissions on the source
contributions can be removed (inner rings in Fig. 6) (Yang et al., 2019).
Without the influence of emission seasonality, local source contributions
decrease in winter and increase in summer, indicating that it was the higher
local anthropogenic emissions that result in the larger local source
contributions to wintertime aerosols in Europe relative to other seasons.
Sulfur sources over oceans account for one-fourth to one-third of the European
sulfate concentration and burden in spring likely due to the strong
westerlies in this season that transport aerosols from the North Atlantic
Ocean to the Europe. Source contributions from Russia–Belarus–Ukraine and
North America to BC and POA in Europe show strong seasonal variabilities,
which can be explained by the changes in biomass burning emissions
considering their large seasonal variability.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6"><?xmltex \currentcnt{6}?><label>Figure 6</label><caption><p id="d1e1582">Relative contributions (in percent) by emissions from major
tagged source regions to near-surface concentrations (conc.) and column
burdens of December–January–February (DJF), March–April–May (MAM),
June–July–August (JJA), and September–October–November (SON) mean sulfate, BC,
and POA over Europe; averaged over 2010–2018. Outer rings represent the
modeled values, and the relative contributions in inner rings are calculated
based on absolute values normalized by the ratio of seasonal emission to
annual mean. Values larger than 5 % are marked.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f06.png"/>

      </fig>

</sec>
<sec id="Ch1.S5">
  <label>5</label><title>Source apportionment of long-term trends</title>
      <p id="d1e1600">Total sulfate-BC-POA concentrations decreased during 1980–2018 over all of
the four subregions of Europe (Fig. 7) since near-surface aerosol
concentrations in Europe are dominated by its local emissions and the
European anthropogenic emissions have significantly decreased during this
time period. Averaged over the entirety of Europe, near-surface concentrations of
sulfate, BC, and POA decreased by 70 %, 43 %, and 23 %, respectively, between 1980–1984 and 2014–2018, which is consistent with the decreases in local emissions (Table 1). The total sulfate-BC-POA concentrations decreased
by 62 %. With SOA included, this value does not have a substantial change
(from 62 % to 59 %), and the decreasing trends in the four subregions of the Europe are not largely affected either. The column burden of sulfate,
BC, POA, and the sum of these three decreased by 60 %, 28 %, 4 %, and 55 %, respectively, which are less than the decreases in corresponding
near-surface concentrations. It is because nonlocal emissions have larger
influences at high altitudes than at the surface, which partly dampened the
contribution of near-surface aerosol decrease (induced by reductions in emissions) to the column integration.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7"><?xmltex \currentcnt{7}?><label>Figure 7</label><caption><p id="d1e1605">Time series (1980–2018) of absolute (<bold>a, b, c,</bold> and <bold>d</bold>; in micrograms per cubic meter) and relative (<bold>e, f, g,</bold> and <bold>h</bold>; in percent) contributions of emissions from major
source regions to the simulated annual mean near-surface sulfate-BC-POA
concentrations averaged over the four subregions of Europe. Dashed lines in panels
<bold>(a–d)</bold> represent simulated aerosol concentrations including SOA.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f07.png"/>

      </fig>

      <p id="d1e1629">The decrease in local European emissions explains 93 % of the reduced
concentration and 91 % of the reduced burden in Europe between the first
and last 5-year period of 1980–2018, while 8 %–9 % is contributed
by the reduction in emissions from Russia–Belarus–Ukraine (Table 2). The
decrease in emissions from North America also explains 10 % of the reduced
column burden of sulfate-BC-POA in Europe from 1980–1984 to 2014–2018.
Along with the decreases in local<?pagebreak page2586?> emission contributions to near-surface
sulfate-BC-POA concentrations in Europe, the fraction of non-European
emission contributions increased from 10 %–30 % to 30 %–50 %
during 1980–2018 (Fig. 7), indicating that aerosols from foreign
emissions have become increasingly important to
air quality in Europe through long-range transport. Regulations for further improvement of air quality in
Europe in the near future need to take changes in non-European emissions
into account.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2"><?xmltex \currentcnt{2}?><label>Table 2</label><caption><p id="d1e1636">Relative contributions (in percent) of emissions from
major source regions to the changes in near-surface concentrations, column
burden, AOD, and DRF in Europe between 1980–1984 and 2014–2018.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:thead>
       <oasis:row>

         <oasis:entry colname="col1"/>

         <oasis:entry rowsep="1" namest="col2" nameend="col5" align="center">Sulfate-BC-POA </oasis:entry>

       </oasis:row>
       <oasis:row rowsep="1">

         <oasis:entry colname="col1"/>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M83" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> Conc.</oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M84" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> Burden</oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M85" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> AOD</oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>

         <oasis:entry colname="col1">EUR</oasis:entry>

         <oasis:entry colname="col2">92.8</oasis:entry>

         <oasis:entry colname="col3">91.2</oasis:entry>

         <oasis:entry colname="col4">91.2</oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">NAM</oasis:entry>

         <oasis:entry colname="col2">1.8</oasis:entry>

         <oasis:entry colname="col3">10.0</oasis:entry>

         <oasis:entry colname="col4">6.5</oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">NAF</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M86" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.0</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M87" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.5</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M88" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.6</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">MDE</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M89" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.9</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M90" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.9</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M91" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.5</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">EAS</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M92" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M93" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3.1</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M94" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.7</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">RBU</oasis:entry>

         <oasis:entry colname="col2">8.0</oasis:entry>

         <oasis:entry colname="col3">9.2</oasis:entry>

         <oasis:entry colname="col4">8.5</oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">OTH</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M95" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.1</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M96" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4.2</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M97" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2.0</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row rowsep="1">

         <oasis:entry colname="col1">OCN</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M98" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3">0.2</oasis:entry>

         <oasis:entry colname="col4">0.6</oasis:entry>

         <oasis:entry colname="col5"/>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1"/>

         <oasis:entry rowsep="1" namest="col2" nameend="col5" align="center">Sulfate </oasis:entry>

       </oasis:row>
       <oasis:row rowsep="1">

         <oasis:entry colname="col1"/>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M99" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> Conc.</oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M100" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> Burden</oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M101" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> AOD</oasis:entry>

         <oasis:entry colname="col5"><inline-formula><mml:math id="M102" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula> DRF</oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">EUR</oasis:entry>

         <oasis:entry colname="col2">91.3</oasis:entry>

         <oasis:entry colname="col3">89.2</oasis:entry>

         <oasis:entry colname="col4">88.9</oasis:entry>

         <oasis:entry colname="col5">88.2</oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">NAM</oasis:entry>

         <oasis:entry colname="col2">2.1</oasis:entry>

         <oasis:entry colname="col3">10.5</oasis:entry>

         <oasis:entry colname="col4">6.9</oasis:entry>

         <oasis:entry colname="col5" morerows="6">11.8</oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">NAF</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M103" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.6</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M104" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.9</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M105" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.8</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">MDE</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M106" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.8</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M107" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.7</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M108" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">EAS</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M109" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M110" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2.8</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M111" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.4</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">RBU</oasis:entry>

         <oasis:entry colname="col2">8.6</oasis:entry>

         <oasis:entry colname="col3">9.5</oasis:entry>

         <oasis:entry colname="col4">8.7</oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">OTH</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M112" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.1</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3"><inline-formula><mml:math id="M113" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4.0</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col4"><inline-formula><mml:math id="M114" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.8</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

       </oasis:row>
       <oasis:row>

         <oasis:entry colname="col1">OCN</oasis:entry>

         <oasis:entry colname="col2"><inline-formula><mml:math id="M115" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>

         <oasis:entry colname="col3">0.3</oasis:entry>

         <oasis:entry colname="col4">0.7</oasis:entry>

       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e2225">Similar to the declining trend in column burden, simulated total AOD also
decreased from 0.12–0.16 to 0.06–0.08 in NW Europe and SW Europe and from
0.19–0.21 to 0.09–0.13 in E Europe and the GTC region during the past 4
decades (Fig. 8). Sulfate AOD accounts for the largest portion of total
combustion AOD (sum of sulfate, BC, POA, and SOA) over the four subregions
of Europe. The combustion AOD has decreased by 0.065 from 1980–1984 to
2014–2018 (Table 1), with 0.059 (91 %) contributed by the decrease in
sulfate AOD. Therefore, we focus on sulfate aerosol when examining the
decadal changes in AOD and DRF in Europe below.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8"><?xmltex \currentcnt{8}?><label>Figure 8</label><caption><p id="d1e2230">Time series (1980–2018) of simulated annual mean AOD for
sulfate, BC, POA, SOA, dust, and sea salt averaged over the four subregions
of Europe. Dashed lines represent AOD from AERONET measurements.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f08.png"/>

      </fig>

      <p id="d1e2239">The decreased sulfate AOD can also be decomposed into different
contributions from individual source regions in CAM5-EAST. Local European
emissions contribute to 89 % of the decrease, followed by 9 % and 7 %
attributed to changes in emissions from Russia–Belarus–Ukraine and North
America, respectively, with the residual offset by other<?pagebreak page2587?> source regions
(Table 2). Over the last 4 decades, model-simulated sulfate AOD decreased
at rates of 0.017, 0.017, 0.026, and 0.012 per decade, respectively, over
NW Europe, SW Europe, E Europe, and GTC. Decreases in local European
<inline-formula><mml:math id="M116" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> emissions result in 78 % of the sulfate AOD decreases over GTC
and about 90 % over the other three subregions (Fig. 9). For the remote
sources, emission changes in North America explain 5 %–10 % of the
European sulfate AOD decrease, while Russia–Belarus–Ukraine sources
contribute 29 % of the sulfate AOD decrease over GTC and 6 %–8 % over
NW Europe and E Europe, indicating a possible warming enhancement effect of
changes in emissions from North America and Russia–Belarus–Ukraine.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9"><?xmltex \currentcnt{9}?><label>Figure 9</label><caption><p id="d1e2255">Absolute contributions (per decade) of the emissions
from major source regions to the trends of sulfate AOD over the four
subregions of Europe. Error bars represent 95 % confidence intervals of
the linear regression.</p></caption>
        <?xmltex \igopts{width=199.169291pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f09.png"/>

      </fig>

      <p id="d1e2265">Averaged over 1980–2018, sulfate imposed a cooling effect over Europe, with
the maximum negative DRF at the top of the atmosphere (TOA) exceeding <inline-formula><mml:math id="M117" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:math></inline-formula> W m<inline-formula><mml:math id="M118" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in E Europe (Fig. 10). Compared to 1980–1984, the magnitude of
sulfate DRF decreased in 2014–2018, leading to a 1–3 W m<inline-formula><mml:math id="M119" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> warming
mainly in E Europe. The warming effect mostly came from a local <inline-formula><mml:math id="M120" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
emission reduction, while non-European emission changes only contributed
less than 0.4 W m<inline-formula><mml:math id="M121" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> over most regions of the Europe. Considering Europe
as a whole, the decrease in sulfate DRF caused a warming effect of 2.0 W m<inline-formula><mml:math id="M122" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>, with 88 % and 12 % coming from reductions in local European
emissions and changes in non-European emissions, respectively (Tables 1 and 2).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F10"><?xmltex \currentcnt{10}?><label>Figure 10</label><caption><p id="d1e2340"><bold>(a)</bold> Simulated annual mean DRF (in watts per square meter) of sulfate
averaged over 1980–2018 and <bold>(b)</bold> the difference in sulfate DRF between
1980–1984 and 2014–2018. The contributions of European and non-European
emissions to the difference are given in <bold>(c)</bold> and <bold>(d)</bold>, respectively.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f10.png"/>

      </fig>

      <p id="d1e2360">Future changes in sulfate DRF associated with European and non-European
emissions based on eight SSP scenarios are also estimated and shown in
Fig. 11, and Fig. S4 gives an estimate for each SSP scenario. Sulfate
DRF contributed by both European and non-European emissions would decrease
in the near future but has large variabilities between different SSPs. The
sulfate DRF (cooling) over Europe contributed from local European emissions
shows a decrease from <inline-formula><mml:math id="M123" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.48</mml:mn></mml:mrow></mml:math></inline-formula> W m<inline-formula><mml:math id="M124" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the year 2015 to <inline-formula><mml:math id="M125" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.18</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M126" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.08</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M127" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.33</mml:mn></mml:mrow></mml:math></inline-formula>) W m<inline-formula><mml:math id="M128" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the year 2030 and <inline-formula><mml:math id="M129" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.14</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M130" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.05</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M131" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.29</mml:mn></mml:mrow></mml:math></inline-formula>) W m<inline-formula><mml:math id="M132" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the year 2050. Unlike their contributions
to the historical (1980–2018) change, non-European emissions have an
increasingly significant impact on the future sulfate DRF changes in Europe.
The contributions of non-European emissions decrease from <inline-formula><mml:math id="M133" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.68</mml:mn></mml:mrow></mml:math></inline-formula> W m<inline-formula><mml:math id="M134" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>
in the year 2015 to <inline-formula><mml:math id="M135" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.39</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M136" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.13</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M137" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.64</mml:mn></mml:mrow></mml:math></inline-formula>) W m<inline-formula><mml:math id="M138" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the year 2030
and <inline-formula><mml:math id="M139" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.26</mml:mn></mml:mrow></mml:math></inline-formula> (<inline-formula><mml:math id="M140" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.08</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M141" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.63</mml:mn></mml:mrow></mml:math></inline-formula>) W m<inline-formula><mml:math id="M142" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> in the year 2050, with changes of a magnitude similar to that of local European emissions. It
suggests that future changes in non-European emissions are as important as
European emissions to radiative balance and associated regional climate
change in Europe.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F11"><?xmltex \currentcnt{11}?><label>Figure 11</label><caption><p id="d1e2580">Time series (2015–2050) of estimated annual mean
sulfate DRF over Europe contributed by European and non-European emissions.
Lines and areas represent median values and minimum-to-maximum ranges of the
estimated sulfate DRF from eight SSP scenarios, including SSP1-1.9,
SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, and SSP5-8.5.
Future DRF of sulfate aerosol over Europe is estimated by scaling historical
mean (1980–2018) sulfate DRF using the ratio of SSPs future <inline-formula><mml:math id="M143" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">SO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
emissions to historical emissions and assuming a linear response of DRF to
regional emissions.</p></caption>
        <?xmltex \igopts{width=199.169291pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/2579/2020/acp-20-2579-2020-f11.png"/>

      </fig>

</sec>
<sec id="Ch1.S6" sec-type="conclusions">
  <label>6</label><title>Conclusions</title>
      <p id="d1e2608">Using a global aerosol–climate model with an explicit aerosol source tagging
technique (CAM5-EAST), we examine the long-term trends and source
apportionment of aerosols in Europe for 1980–2018 from 16 source
regions covering the globe in this study. CAM5-EAST can capture well the
spatial distribution and temporal variation in aerosol species in Europe
during this time period.</p>
      <p id="d1e2611">Averaged over 2010–2018, European emissions account for 54 %–68 %,
78 %–95 %, and 58 %–78 % of near-surface sulfate, BC, and POA
concentrations over Europe, respectively. Russia–Belarus–Ukraine emissions
explain 10 % of sulfate in E Europe and GTC. Northern Africa contributes to
17 % of BC and 24 % of POA over SW Europe. Anthropogenic emissions over
oceans (e.g., from international shipping) and natural emissions from marine
and volcanic<?pagebreak page2588?> activities together account for 16 %–28 % of sulfate
near-surface concentrations in Europe. European emissions only account for
32 %–47 %, 57 %–75 %, and 51 %–71 % of column burden of
sulfate, BC, and POA, respectively, in Europe, with the rest contributed by
emissions from East Asia, Russia–Belarus–Ukraine, northern Africa and North
America. Source contributions of aerosols in Europe vary with seasons, driven
by the seasonality of emissions and meteorology.</p>
      <p id="d1e2614">Compared to 1980–1984, simulated total sulfate-BC-POA near-surface
concentration and column burden for 2014–2018 had a decrease of 62 % and
55 %, respectively, the majority of which was contributed to by reductions in
local European emissions. The decrease in emissions from
Russia–Belarus–Ukraine contributed 8 %–9 % of the near-surface
concentration decrease, while the decrease in emissions from North America
accounted for 10 % of the reduced column burden. With the large decrease
in local emission contributions, aerosols from foreign sources became
increasingly important to air quality in Europe. The decrease in sulfate led
to a 2.0 W m<inline-formula><mml:math id="M144" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> warming in Europe, with 12 % coming from changes in
non-European emissions, especially in North America and
Russia–Belarus–Ukraine. Based on the SSP scenarios and the assumed
relationship between DRF and emissions, we estimated that sulfate DRF over
Europe that was contributed from European emissions and non-European emissions should
decrease at a comparable rate in the near future. This suggests that future
changes in non-European emissions are as important as European emissions in
affecting regional climate change associated with aerosols in Europe. It
should also be noted that the model currently does not have the ability to
simulate nitrate and ammonium aerosols, and, therefore, the conclusions may
not hold with all aerosols.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e2633">TheCAM5-EAST model code and results can be made available through the National Energy Research Scientific Computing Center (NERSC) servers upon request.</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e2636">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/acp-20-2579-2020-supplement" xlink:title="pdf">https://doi.org/10.5194/acp-20-2579-2020-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e2645">YY, SL, and HW designed the research; YY performed the model simulations;
YY and SL analyzed the data. All the authors discussed the results and
wrote the paper.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e2651">The authors declare that they have no conflict of interest.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e2657">This research was support by the National Natural Science Foundation of
China under grant 41975159, the U.S. Department of Energy (DOE), Office of
Science, Biological and Environmental Research as part of the Earth and
Environmental System Modeling program, Jiangsu Specially Appointed Professor
Project, and the Startup Fund for Talent at NUIST under grant 2019r047. The
Pacific Northwest National Laboratory is operated for the DOE by Battelle
Memorial Institute under contract DE-AC05-76RLO1830. The National Energy
Research Scientific Computing Center (NERSC) provided computational support.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e2662">This research has been supported by the National Natural Science Foundation of China (grant no. 41975159).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e2668">This paper was edited by Qiang Zhang and reviewed by three anonymous referees.</p>
  </notes><ref-list>
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    <!--<article-title-html>Trends and source apportionment of aerosols in Europe during 1980–2018</article-title-html>
<abstract-html><p>Aerosols have significantly affected health, environment, and climate in
Europe. Aerosol concentrations have been declining since the 1980s in Europe,
mainly owing to a reduction of local aerosol and precursor emissions.
Emissions from other source regions of the world, which have been changing
rapidly as well, may also perturb the historical and future trends of
aerosols and change their radiative impact in Europe. This study examines
trends of aerosols in Europe during 1980–2018 and quantifies contributions
from 16 source regions using the Community Atmosphere Model version 5
with Explicit Aerosol Source Tagging (CAM5-EAST). The simulated
near-surface total mass concentration of sulfate, black carbon, and primary
organic carbon had a 62&thinsp;% decrease during 1980–2018. The majority of which was contributed to reductions of local emissions in Europe, and
8&thinsp;%–9&thinsp;% was induced by a decrease in emissions from
Russia–Belarus–Ukraine. With the decreases in the fractional contribution of
local emissions, aerosols transported from other source regions are
increasingly important for air quality in Europe. During 1980–2018, the
decrease in sulfate loading led to a warming effect of 2.0&thinsp;W&thinsp;m<sup>−2</sup> in
Europe, with 12&thinsp;% coming from changes in non-European sources, especially
from North America and Russia–Belarus–Ukraine. According to the Shared
Socioeconomic Pathways (SSP) scenarios, contributions to the sulfate
radiative forcing over Europe from both local European emissions and
non-European emissions should decrease at a comparable rate in the next 3
decades, suggesting that future changes in non-European emissions are as
important as European emissions for causing possible regional climate change
associated with aerosols in Europe.</p></abstract-html>
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