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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-20-45-2020</article-id><title-group><article-title>Rapid formation of intense haze episodes via aerosol–boundary<?xmltex \hack{\break}?> layer feedback in Beijing</article-title><alt-title>Rapid formation of intense haze episodes in Beijing</alt-title>
      </title-group><?xmltex \runningtitle{Rapid formation of intense haze episodes in Beijing}?><?xmltex \runningauthor{Y. Wang et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Wang</surname><given-names>Yonghong</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2498-9143</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Yu</surname><given-names>Miao</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff6">
          <name><surname>Wang</surname><given-names>Yuesi</given-names></name>
          <email>wys@mail.iap.ac.cn</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Tang</surname><given-names>Guiqian</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Song</surname><given-names>Tao</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Zhou</surname><given-names>Putian</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-0803-7337</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Liu</surname><given-names>Zirui</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1939-9715</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Hu</surname><given-names>Bo</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-4808-9115</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Ji</surname><given-names>Dongsheng</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Lili</given-names></name>
          
        <ext-link>https://orcid.org/0000-0003-2308-7404</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Zhu</surname><given-names>Xiaowan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Yan</surname><given-names>Chao</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-5735-9597</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Ehn</surname><given-names>Mikael</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-0215-4893</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Gao</surname><given-names>Wenkang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Pan</surname><given-names>Yuepeng</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-5547-0849</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff6">
          <name><surname>Xin</surname><given-names>Jinyuan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Sun</surname><given-names>Yang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Kerminen</surname><given-names>Veli-Matti</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-0706-669X</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff2 aff4 aff5">
          <name><surname>Kulmala</surname><given-names>Markku</given-names></name>
          <email>markku.kulmala@helsinki.fi</email>
        <ext-link>https://orcid.org/0000-0003-3464-7825</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff4 aff5">
          <name><surname>Petäjä</surname><given-names>Tuukka</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-1881-9044</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry (LAPC),<?xmltex \hack{\break}?> Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing 100029, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, P.O. Box 64,<?xmltex \hack{\break}?> 00014 University of Helsinki, Helsinki, Finland</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Institute of Urban Meteorology, China Meteorological Administration, Beijing, China</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Joint international research Laboratory of Atmospheric and Earth
SysTem sciences (JirLATEST), Nanjing University, Nanjing, China</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for
Soft Matter Science and Engineering,<?xmltex \hack{\break}?> Beijing University of Chemical
Technology (BUCT), Beijing, China</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Centre for Excellence in Atmospheric Urban Environment, Institute of Urban Environment, Chinese Academy of Science, Xiamen, Fujian 361021, China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Yuesi Wang (wys@mail.iap.ac.cn) and Markku Kulmala (markku.kulmala@helsinki.fi)</corresp></author-notes><pub-date><day>3</day><month>January</month><year>2020</year></pub-date>
      
      <volume>20</volume>
      <issue>1</issue>
      <fpage>45</fpage><lpage>53</lpage>
      <history>
        <date date-type="received"><day>10</day><month>October</month><year>2018</year></date>
           <date date-type="rev-request"><day>6</day><month>November</month><year>2018</year></date>
           <date date-type="rev-recd"><day>22</day><month>September</month><year>2019</year></date>
           <date date-type="accepted"><day>4</day><month>November</month><year>2019</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="d1e299">Although much effort has been put into studying air pollution, our knowledge
of the mechanisms of frequently occurring intense haze episodes in China is
still limited. In this study, using 3 years of measurements of air
pollutants at three different height levels on a 325 m Beijing
meteorology tower, we found that a positive aerosol–boundary layer feedback
mechanism existed at three vertical observation heights during intense haze
polluted periods within the mixing layer. This feedback was characterized by
a higher loading of PM<inline-formula><mml:math id="M1" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> with a shallower mixing layer. Modelling
results indicated that the presence of PM<inline-formula><mml:math id="M2" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> within the boundary layer led
to reduced surface temperature, relative humidity and mixing layer height
during an intensive haze episode. Measurements showed that the
aerosol–boundary layer feedback was related to the decrease in solar
radiation, turbulent kinetic energy and thereby suppression of the mixing
layer. The feedback mechanism can explain the rapid formation of intense
haze episodes to some extent, and we suggest that the detailed feedback
mechanism warrants further investigation from both model simulations and
field observations.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

      <?xmltex \hack{\newpage}?>
<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e331">With the rapid economic growth and urbanization, an increasing frequency of
haze episodes along with the air pollution has become of great concern in
China during the last decade (Cao et al., 2016; Huang et al., 2014;
Kulmala, 2015; Y. H. Wang et al., 2014, 2015). For example, during
December 2016 a series of intense haze episodes took place in eastern China,
characterized by surface PM<inline-formula><mml:math id="M3" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations exceeding 500 <inline-formula><mml:math id="M4" 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="M5" 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 several measurement sites in Beijing and its surrounding sites
(<uri>http://www.mep.gov.cn/gkml/hbb/qt/201701/t20170102_393745.htm</uri>, last access: 11 December 2019). Severe air pollution has serious effects on human health. A
recent study reported that the particulate matter has significantly
decreased the life span of residents by as many as 5.5 years in northern China
(Chen et al., 2013). On a global scale, the air pollution was estimated
to cause over 3 million premature deaths every year (Lelieveld et al.,
2015).</p>
      <p id="d1e366">Increased emissions from fossil fuel combustion due to vehicle traffic,
industrial activities and power generation, along with exceptionally strong
secondary aerosol formation, were<?pagebreak page46?> thought to be responsible for these haze
episodes (Cheng et al., 2016; Huang et al., 2014; Pan et al., 2016;
Petäjä et al., 2016;  Zhang et al., 2015; Zhao
et al., 2013). Meanwhile, the formation of intense haze episodes was
considered to be affected by meteorological conditions (J. Wang et al., 2014;
Quan et al., 2013; Zheng et al., 2016). For example, the
mixing layer height is a key parameter that constrains the dilution of
surface air pollution, and the development of the mixing layer is highly related
to the amount of solar radiation absorbed by the air and reaching the
surface (Ding et al., 2016; Stull, 1988; Sun et al., 2013; Tang et al.,
2016). By using field measurements combined with model
simulation, a positive feedback between aerosol pollution, relative humidity
and boundary layer was found to be important in aerosol production,
accumulation and severe haze formation in Beijing (Liu et al., 2018). Wang
et al. (2018) found that PBL schemes in their atmospheric chemistry models
are not sufficient to describe the explosive growth of PM<inline-formula><mml:math id="M6" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations
in the Beijing–Tianjin–Hebei region due to the absence of an online calculation of
aerosol–radiation feedback and/or a deficient description of extremely weak
turbulent diffusion.</p>
      <p id="d1e378">In this study, using unique measurements on the Beijing 325 m high
meteorology tower, we show a clear relationship between mixing layer height
and turbulent kinetic energy at the 140 m observation platform. We also
present direct evidence of the feedback that relates the decreasing mixed
layer height to increasing particulate matter concentrations, and this
feedback is critical to the formation of intense haze episodes in Beijing.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Calculation of mixing layer height with ceilometer</title>
      <p id="d1e396">The ceilometer was deployed in the yard of the IAP (Institute of Atmospheric
Physics, Chinese Academy of Science), with a horizontal distance of around tens
of metres from the 325 m meteorology tower. The mixing layer height was
measured with the enhanced single-lens ceilometers from July 2009 to
August 2012 (CL 31, Vaisala, Finland), which utilized the strobe laser
lidar technique (910 nm) to measure the attenuated backscattering
coefficient profiles. The detection range of the CL31 is 7.6 km with the report
period of 2–120 s. Detailed information can be found in previous studies (Tang
et al., 2016). Since the distribution of particle concentrations is uniform
in the mixing layer and has significant differences between the mixing layer
and free atmosphere, the height at which a sudden change exists in the
attenuated backscattering coefficient profile indicates the top of the
mixing layer height. Vaisala software product BL-VIEW was used to
determine the mixing layer height by finding the position with the maximum
negative gradient (<inline-formula><mml:math id="M7" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mi mathvariant="normal">d</mml:mi><mml:mi mathvariant="italic">β</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:math></inline-formula>) in the attenuated backscattering
coefficient profiles as the top of the mixing layer
(Münkel et al., 2007).</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><?xmltex \opttitle{Measurements of energy flux at the 325\,m Beijing meteorology tower}?><title>Measurements of energy flux at the 325 m Beijing meteorology tower</title>
      <p id="d1e427">The turbulent fluxes of sensible heat (<inline-formula><mml:math id="M8" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">H</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>), latent heat (<inline-formula><mml:math id="M9" display="inline"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi mathvariant="normal">E</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) and
turbulence kinetic energy (TKE) were measured at the 140 m level using
the eddy covariance technique from July 2009 to August 2012. The raw data
(10 Hz) of wind components (<inline-formula><mml:math id="M10" display="inline"><mml:mi>u</mml:mi></mml:math></inline-formula>, <inline-formula><mml:math id="M11" display="inline"><mml:mi>v</mml:mi></mml:math></inline-formula> and <inline-formula><mml:math id="M12" display="inline"><mml:mi>w</mml:mi></mml:math></inline-formula>) and sonic temperature (<inline-formula><mml:math id="M13" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) was recorded
with three-dimensional sonic anemometers (Model CSAT3, Campbell Scientific
Inc., Logan, Utah, USA), and of water vapour concentrations (<inline-formula><mml:math id="M14" display="inline"><mml:mi>q</mml:mi></mml:math></inline-formula>) with open-path
infrared gas analysers (Model LI-7500, LiCor Inc., Lincoln, Nebraska, USA).
The fluxes of heat (<inline-formula><mml:math id="M15" display="inline"><mml:mi>Q</mml:mi></mml:math></inline-formula>) were calculated as the covariance between the
instantaneous deviation or fluctuations of vertical velocity
(<inline-formula><mml:math id="M16" display="inline"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mo>′</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>) and their respective scalar
(<inline-formula><mml:math id="M17" display="inline"><mml:mrow><mml:msubsup><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mo>′</mml:mo></mml:msubsup></mml:mrow></mml:math></inline-formula>) averaged over a time interval of 30 min:
            <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M18" display="block"><mml:mrow><mml:mi>Q</mml:mi><mml:mo>=</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>w</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msup><mml:mi>s</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mn mathvariant="normal">1</mml:mn><mml:mi>N</mml:mi></mml:mfrac></mml:mstyle><mml:munderover><mml:mo movablelimits="false">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:munderover><mml:msup><mml:mi>w</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msup><mml:mi>s</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where the overbar denotes a time average, <inline-formula><mml:math id="M19" display="inline"><mml:mi>N</mml:mi></mml:math></inline-formula> is the number of samples during
the averaging time and the fluctuations are the differences between the
instantaneous readings and their respective means. The TKE was calculated
as follows (Stull, 1988):
            <disp-formula id="Ch1.E2" content-type="numbered"><label>2</label><mml:math id="M20" display="block"><mml:mrow><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mi mathvariant="normal">TKE</mml:mi><mml:mi>m</mml:mi></mml:mfrac></mml:mstyle><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mn mathvariant="normal">1</mml:mn><mml:mn mathvariant="normal">2</mml:mn></mml:mfrac></mml:mstyle><mml:mfenced close=")" open="("><mml:mrow><mml:msup><mml:mover accent="true"><mml:mi>u</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>′</mml:mo></mml:msup><mml:mo>+</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>v</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>+</mml:mo><mml:mover accent="true"><mml:mrow><mml:msup><mml:mi>w</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:mrow><mml:mo mathvariant="normal">‾</mml:mo></mml:mover></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mover accent="true"><mml:mi>e</mml:mi><mml:mo mathvariant="normal">‾</mml:mo></mml:mover><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>
          where <inline-formula><mml:math id="M21" display="inline"><mml:mi>m</mml:mi></mml:math></inline-formula> is the mass (kg) and <inline-formula><mml:math id="M22" display="inline"><mml:mi>e</mml:mi></mml:math></inline-formula> is the TKE per unit mass (m<inline-formula><mml:math id="M23" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M24" 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>). A more detailed description of the calculation and post-processing of flux is
provided elsewhere (Song et al.,
2013).</p>
</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><?xmltex \opttitle{Measurements of PM${}_{{2.5}}$ concentration and gases at the 325\,m Beijing meteorology tower}?><title>Measurements of PM<inline-formula><mml:math id="M25" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration and gases at the 325 m Beijing meteorology tower</title>
      <p id="d1e703">The mass concentrations of PM<inline-formula><mml:math id="M26" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> at the 8, 120 and 280 m observation
platforms were measured with three TEOM RP1400s simultaneously from July 2009 to August 2012 (Thermo Scientific, <uri>https://www.thermoscientific.com</uri>, last access: 11 December 2019). The resolution and precision of the
instrument for 1 h measurements were 0.1 and <inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mo>±</mml:mo><mml:mn mathvariant="normal">1.5</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M28" 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="M29" 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>, respectively. The filters were exchanged when the
loading rates were approximately 40 %. The flow rate was monitored and
calibrated monthly. The volume mixing ratios of ozone and <inline-formula><mml:math id="M30" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> were measured
with 49i and 42i (Thermal Environment Instruments (TEI) Inc.), respectively
(Y. H. Wang et al., 2014).</p>
</sec>
<sec id="Ch1.S2.SS4">
  <label>2.4</label><title>Experiment design</title>
      <?pagebreak page47?><p id="d1e768">The model used in this study is the Weather Research and Forecasting (WRF)
model (ARW, version 3.8.1; Skamarock et al., 2008). The simulation domain was
centered in Beijing (39.0<inline-formula><mml:math id="M31" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, 116.0<inline-formula><mml:math id="M32" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E) and implemented
with one-nested grids with a resolution of 1 km. The number of grid cells
was <inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:mn mathvariant="normal">460</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">403</mml:mn></mml:mrow></mml:math></inline-formula> for the domain in the east–west and south–north
directions. The model run was initialized at 00:00 UTC (or 08:00 LST) on 16 November 2010 and integrated for 131 h until 10:00 UTC on 21 November 2010, including 48 h
for spin-up. The initial conditions of the model and its outermost lateral
boundary conditions, as well as the soil moisture field, were taken from
National Centers for Environmental Prediction/National Center for
Atmospheric Research Reanalysis data (resolution: 1<inline-formula><mml:math id="M34" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M35" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 1<inline-formula><mml:math id="M36" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>). The model physics schemes used include Thompson
microphysical parameterization (Thompson et al., 2004);
BouLac boundary-layer parameterization (Bougeault and Lacarrere, 1989); the RRTMG
(Iacono et al., 2008) radiation
scheme; and the Building Effect Parameterization (BEP) and Building Energy
Model (BEM) schemes implemented in WRF that can more accurately describe
three-dimensional urban land surface features and processes, including
anthropogenic heat from buildings (Martilli et al., 2002;
Salamanca and Martilli, 2010). The control and test experiments were
performed separately to investigate impact of aerosol direct radiative
forcing on surface temperature, relative humidity and development of
boundary layer height. The control run (CTL) used the RRTMG radiation scheme
which ignored the direct radiation effects of aerosol input. In the sensitivity
test experiment, we add the aerosol input in the RRTMG scheme using Tegen
climatology and urban-type aerosols during the sensitivity test.</p>
</sec>
<sec id="Ch1.S2.SS5">
  <label>2.5</label><title>Other supporting measurements</title>
      <p id="d1e836">Total solar radiation was measured with a direct radiometer (TBQ-2, Junzhou,
China). Direct radiation was measured with a direct radiometer (TBS-2,
Junzhou, China). UV radiation in the range of 220–400 nm was measured using
a CUV3 radiometer (USA). The estimated experiment errors for the three
instruments are 3 %, 1 % and 2 %, respectively. The original data were
obtained at 1 min intervals and the hourly average values were used in
this study. The chemical composition of organic, sulfate, nitrate, ammonium
and chloride in non-refractory submicron aerosol were measured during
several campaigns with an Aerodyne High-Resolution Time-of-Flight Aerosol
Mass Spectrometer from July 2009 to August 2012 (HR-ToF-AMS, Aerodyne
Research Inc., Billerica, MA, USA). Detailed information about the instrument,
calibration and data process have been introduced by Zhang et al. (2016). All these measurements
were conducted at the IAP station.</p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results and discussion</title>
      <p id="d1e848">A typical intense haze episode occurred during the heating season in urban
Beijing during 17 to 22 November 2010. This episode was associated with
synoptic stagnation in the North China Plain (Fig. S1 in the Supplement) and was
characterized by low wind speeds and irregular wind direction (Fig. 1).
Several meteorological variables had distinct temporal patterns during
different stages of pollution, including reduced solar radiation and
increased relative humidity during the most intense presence of haze (Fig. 1). The temporal patterns of PM<inline-formula><mml:math id="M37" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations were very similar
at the two lower measurement heights (8 and 120 m, Fig. 1d), even though
the concentration was clearly highest close to the surface. The
PM<inline-formula><mml:math id="M38" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration measured at 280 m behaved in a different way,
especially during the most intense period of the haze when the mixed layer
height was very low (Fig. 1e). The decoupling of the 280 m platform from the
other two lower ones at low mixed layer heights is apparent in our 3-year
measurement data set, especially when comparing <inline-formula><mml:math id="M39" 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> and <inline-formula><mml:math id="M40" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>
concentrations between the three measurement platforms (Figs. S2 and S5).
During the haze period, the maximum PM<inline-formula><mml:math id="M41" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations at 8, 120 and
280 m were 505, 267 and 339 <inline-formula><mml:math id="M42" 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="M43" 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>, respectively. The higher
maximum concentration at 280 m compared with 120 m can be ascribed to
the transport of pollutants from the surrounding regions of Hebei and Tianjin
provinces typical for polluted periods (Sun et al., 2013). The
mixing layer height varied from 130 to 1640 m during the haze episode,
ranging between about 200 and 500 m during the most intense period of the
haze period on 18 November 2010 (Fig. 1e). The TKE was quite low during this
intensive haze episode from 18 to 21 November, with an average
value around 0.3 m<inline-formula><mml:math id="M44" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M45" 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>. However, the TKE increased significantly
on the morning of 21 November as surface wind increased from 1.2 to around 6 m s<inline-formula><mml:math id="M46" 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>, which was possible due to the movement of a cold front as shown in Fig. S1.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><label>Figure 1</label><caption><p id="d1e956">Measurements of <bold>(a)</bold> solar radiation and ultraviolet
radiation at 8 m, <bold>(b)</bold> wind speed and direction at 8 m, <bold>(c)</bold> relative humidity
and air temperature at 8 m, <bold>(d)</bold> mass concentration of PM<inline-formula><mml:math id="M47" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> at 8, 120 and 280 m, and <bold>(e)</bold> mixing layer height at 8 m and turbulence kinetic energy at
140 m in the Beijing 325 m meteorology tower during an intensive air
pollution episode in November 2010. The evolution of the air pollution
episode can be divided into period 1 (clean period to air pollution
accumulation period), period 2 (pollution period) and period 3 (pollution to
clean period).</p></caption>
        <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/45/2020/acp-20-45-2020-f01.png"/>

      </fig>

      <p id="d1e990">The vertical distributions of attenuated backscatter density obtained from
ceilometer measurements indicate vertical mixing conditions accompanied by
an inversion layer and high relative humidity in the surface as shown in
Fig. 2. The strong inversion and high relative humidity occurred on
the morning of 18 November 2010, with a lapse rate of 2 K / 100 m, relative
humidity of 78 % and a northerly wind speed of around 2 m s<inline-formula><mml:math id="M48" 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> detected
by the vertical sounding. The turbulent kinetic energy at 140 m was reduced
to around 0.1–0.7 m<inline-formula><mml:math id="M49" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M50" 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> due to decreased solar
radiation, as presented in Fig. 1a. In this manner, the development of a
mixing layer was significantly suppressed during the intense haze episode.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><label>Figure 2</label><caption><p id="d1e1029">Observed attenuated backscatter density, calculated
mixing layer height using ceilometer and vertical wind speed, wind
direction, relative humidity, and virtual potential temperature using sounding
data during 18 November <bold>(a)</bold> and 19 November <bold>(b)</bold>. The black flags on the left-
and right-hand sides of the figures stand for vertical wind speed and wind
direction obtained from sounding measurements at 08:00 and 20:00 Beijing
time, respectively. The circle on the left-hand side of the figure represents calm
wind. The dotted yellow lines and solid green lines represent the vertical
distribution of virtual potential temperature and relative humidity from
sounding at 08:00 and 20:00, respectively. The yellow square and green
square represent the first layer and second layer, respectively, and usually the
first layer was used as the mixing layer height. The mixing layer height was
determined from the local minimum of the backscatter density gradient, and
the colour in the figure stands for backscatter density from the ceilometer.
From both figures, we can clearly see that the mixing layer has an important role
in regulating the distribution of air pollutants.</p></caption>
        <?xmltex \igopts{width=469.470472pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/45/2020/acp-20-45-2020-f02.png"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><label>Figure 3</label><caption><p id="d1e1046"><bold>(a)</bold> Modelled variation of surface relative humidity and temperature and
<bold>(b)</bold> mixing layer height during the intensive haze episode from 18  to 21 November 2010. The lines with triangles on them represent
results from the test experiment, while the lines without triangles represent results from the control
experiment. The control experiment was performed with the absence of aerosol
direct radiative forcing in the RRTMG radiation scheme, while the test
experiment was conducted with the presence of aerosol direct radiative forcing
considered.</p></caption>
        <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/45/2020/acp-20-45-2020-f03.png"/>

      </fig>

      <p id="d1e1060">In order to demonstrate how the PM<inline-formula><mml:math id="M51" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> modifies the surface temperature,
relative humidity and development of the mixing layer height, we performed
two numerical simulation experiments, using the WRF model as a tool. We took
the measurements during the intensive haze episode shown in Fig. 1 as an
example. As shown in Fig. 3a, the variation of temperature and relative
humidity showed pronounced daily variations, with higher and lower values,
respectively, during daytime in both test and control experiments. However,
the presence of aerosol in the test experiment clearly showed decreased
surface temperature and increased relative humidity. The presence of aerosol
reduces downward radiation reaching the surface, as a result of which the
surface temperature and sensitive heat flux decrease and the development of
the mixing layer height is suppressed (M. Li et al., 2017; Z. Li et al., 2017; Miao et al., 2016). Statistical results showed that
the average relative humidity, surface temperature and mixing layer height
were <inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mn mathvariant="normal">8.2</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">3.4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M53" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, <inline-formula><mml:math id="M54" display="inline"><mml:mrow><mml:mn mathvariant="normal">40.5</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">11.6</mml:mn></mml:mrow></mml:math></inline-formula> % and <inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:mn mathvariant="normal">377.7</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">499</mml:mn></mml:mrow></mml:math></inline-formula> m, respectively, without the consideration of aerosol direct radiative
forcing, whereas the consideration of aerosol directive radiative forcing
changed these values to <inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:mn mathvariant="normal">7.1</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">3.1</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M57" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, <inline-formula><mml:math id="M58" display="inline"><mml:mrow><mml:mn mathvariant="normal">40.6</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">11.7</mml:mn></mml:mrow></mml:math></inline-formula> %
and <inline-formula><mml:math id="M59" display="inline"><mml:mrow><mml:mn mathvariant="normal">326.7</mml:mn><mml:mo>±</mml:mo><mml:mn mathvariant="normal">470.1</mml:mn></mml:mrow></mml:math></inline-formula> m, respectively. Our model results clearly
demonstrate the pronounced role of<?pagebreak page49?> aerosol particles in reducing the mixing
layer height during this haze pollution episode.</p>
      <p id="d1e1163">In order to further illustrate how the mixing layer height modifies
PM<inline-formula><mml:math id="M60" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations, we used 3 years of simultaneous winter-time
air pollutant measurements in Beijing. We divided the observed
PM<inline-formula><mml:math id="M61" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations into highly polluted and less polluted conditions
using a threshold value of 75 <inline-formula><mml:math id="M62" 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="M63" 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> for PM<inline-formula><mml:math id="M64" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> to distinguish
between these conditions. This is consistent with the Chinese Environment
Protection Bureau definition of a haze pollution event. With this threshold
value, we found that 31 % and 69 % of the total measurement time
corresponded to highly polluted and less polluted conditions, respectively.
We plotted the PM<inline-formula><mml:math id="M65" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> data as a function of the mixing layer height at
the three observation heights (8, 120 and 280 m) during both
highly polluted and less polluted conditions and fitted an exponential curve
to these data based on best fitting (Fig. 4). The PM<inline-formula><mml:math id="M66" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
concentration has a clear anti-correlation with the mixing layer height
during the intense haze episodes. At all the measurement heights, the
PM<inline-formula><mml:math id="M67" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration increased as the mixing layer height decreased, and
this pattern was very strong under polluted conditions (Fig. 4). We also
tested the reciprocal fitting function for the data (Fig. S8). It
overestimated the PM<inline-formula><mml:math id="M68" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration when the mixing layer height was
very low, as compared to the exponential fitting function (Fig. 4). This
also indicates that a much higher PM<inline-formula><mml:math id="M69" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration is needed in
order to obtain a very low mixing layer height without the positive
feedback. This can also be supported by the root-mean-square error (RMSE) of
these two fitting methods. The RMSE of the exponential fitting is much
smaller than the reciprocal fitting in any case (Table S1 in the Supplement).</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F4" specific-use="star"><?xmltex \currentcnt{4}?><label>Figure 4</label><caption><p id="d1e1261">The variability of the PM<inline-formula><mml:math id="M70" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration as a
function of the mixing layer height at 8 m <bold>(a)</bold>, 120 m <bold>(b)</bold> and 280 m <bold>(c)</bold>. The
data related to the upper fitting line represent PM<inline-formula><mml:math id="M71" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations
larger than 75 <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>, while the data related to the lower fitting line
represent PM<inline-formula><mml:math id="M74" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations less than 75 <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>. The dark grey
points represent mean values; the red line represents median values. The
shadowed area corresponds to an increased amount of PM<inline-formula><mml:math id="M77" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> with
decreased mixing layer height assuming that PM<inline-formula><mml:math id="M78" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> has the same
variation pattern under highly polluted conditions as in less polluted
times.</p></caption>
        <?xmltex \igopts{width=312.980315pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/45/2020/acp-20-45-2020-f04.png"/>

      </fig>

      <p id="d1e1367">It is worth noting that the increase was mainly from the PM<inline-formula><mml:math id="M79" display="inline"><mml:msub><mml:mi/><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2.5</mml:mn></mml:mrow></mml:msub></mml:math></inline-formula>
fraction that increased from 42 % to 65 % as the mixing layer height
decreased from more than 1400 m to lower than 300 m (Fig. S4). A major
portion of particulate mass between 1 and 2.5 <inline-formula><mml:math id="M80" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m originates from
secondary aerosol formation processes in urban air (Y. H. Wang et al., 2015;
Zhang et al., 2015). As shown in Fig. S7, the concentration of NR-PM<inline-formula><mml:math id="M81" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:math></inline-formula>
increased significantly from 12.1 to 56.4 <inline-formula><mml:math id="M82" 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="M83" 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>
when the variation of MLH decreased from more than 1400 m to less than 200 m. The reduction in solar radiation reaching the surface due to fine
particle matter reduces the turbulent kinetic energy and the development of
the mixing layer, as shown in Fig. 5. An exponential function between the
turbulent kinetic energy at 140 m and mixing layer height was fitted. Based
on this fit, the MLH roughly doubles from about 400  to 800 m when TKE
increases from 0.1 to 1 m<inline-formula><mml:math id="M84" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> s<inline-formula><mml:math id="M85" 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>. These are
typical values of MLH during polluted conditions in Beijing.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><?xmltex \currentcnt{5}?><label>Figure 5</label><caption><p id="d1e1445">Turbulent kinetic energy at 140 m as a function of mixing
layer height and PM<inline-formula><mml:math id="M86" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations at 120 m from July 2009 to
August 2011. An exponential function was fitted based on best fitting.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/20/45/2020/acp-20-45-2020-f05.png"/>

      </fig>

      <?pagebreak page51?><p id="d1e1463">The reduced sensible heat and TKE due to aerosol particles reduce the
entrainment of relatively dry air into the mixing layer from above, which makes
the air more humid within the mixing layer. This, together with the
decreased surface temperature, increases the relative humidity
(Z. Li et al., 2017). The increased relative
humidity enhances the aerosol water uptake and promotes the formation of
secondary organic and inorganic aerosol via aqueous-phase reactions
(Liu et al., 2018; Wang et al.,
2019), enhancing light scattering and causing further reduction in the
intensity of radiation reaching the surface. All these factors suppress the
development of mixing layer height and enhance the accumulation of air
pollutants within the mixing layer. We ascribe part of the observed increase
in PM<inline-formula><mml:math id="M87" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> and simultaneous decrease in the mixing layer height to the
positive feedback associated with the particulate matter–mixing layer
interaction (Petäjä et al., 2016; Ding et al., 2016), occurring at the
same time as primary emissions and secondary formation are confined into a
smaller volume of air. The feedback occurred at all three observation
platforms and appeared to be most intensive at 8 m. In an urban environment,
<inline-formula><mml:math id="M88" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> originates mainly from local anthropogenic emissions, whereas the
sources of particulate matter include both primary emissions and secondary
formation (Ehn et al., 2014; Jimenez et al., 2009; Zhang et al., 2015;
Zhao et al., 2013). As shown in Fig. S6, the median <inline-formula><mml:math id="M89" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> concentration
at 8 m was 250 % higher under highly polluted conditions compared with
less polluted conditions as the mixing layer height decreased to 100–200 m,
while the corresponding number for the PM<inline-formula><mml:math id="M90" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration was 360 %.</p>
      <p id="d1e1506">The increase in the PM<inline-formula><mml:math id="M91" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration from less polluted to
highly polluted conditions is mainly due to concentrated particulate matter
caused by a decreased mixing layer height, which is accompanied by primary
particle emissions, secondary aerosol formation and feedback from
particulate matter–mixing layer height interactions. Compared with the
increased amounts of <inline-formula><mml:math id="M92" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mi>x</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, we can roughly estimate that a maximum of 110 % of
the increased PM<inline-formula><mml:math id="M93" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> originates from secondary aerosol formation
processes in this study. Of the remaining 250 % of the PM<inline-formula><mml:math id="M94" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
increase, potentially a large fraction originates from particulate
matter–mixing layer height interactions, but we cannot quantify this
fraction at the moment.</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <label>4</label><title>Conclusions</title>
      <p id="d1e1557">The development of a mixing layer height in an urban city is affected by the
intensity of incoming solar radiation. Our measurement at the 325 m
meteorology tower showed that the solar and ultraviolet radiation reaching
the surface decreases considerably at increased pollution levels, which leads
to a decreased TKE and, consequently, the suppression of mixing layer
development. In turn, the shallowed mixing layer height further favours the
enhancement of PM<inline-formula><mml:math id="M95" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration and its precursor gases from both
direct emissions and secondary formation. This feedback mechanism may be an
important reason for the rapid increase in particulate matter from
moderately polluted conditions to periods of intense pollution in an urban
atmosphere as the strength increased when the PM<inline-formula><mml:math id="M96" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration
increased, although we cannot quantify the feedback amount exactly by
observations currently. The particulate matter–mixing layer height feedback
is probably a critical factor in the formation of intense haze periods from
moderately polluted periods in Beijing and other polluted cities.</p>
</sec>

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

      <p id="d1e1582">The readers can access the data by contacting Yonghong Wang (yonghong.wang@helisnkifi) or Yuesi Wang (wys@mail.iap.ac.cn).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e1585">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/acp-20-45-2020-supplement" xlink:title="pdf">https://doi.org/10.5194/acp-20-45-2020-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e1594">MK, TP and YoW had the original idea of the study. YuW, GT, TS,
ZL, BH, LW, XZ, DJ, WG and YS conducted the long-time measurements and
provided the data. MY conducted model simulation. YoW, GT, ST, PZ,
ME, CY, VK, TP and MK interpreted the data and plotted the figures.
YoW wrote the manuscript with contributions from all the co-authors.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e1600">The authors declare that they have no conflict of interest.</p>
  </notes><notes notes-type="sistatement"><title>Special issue statement</title>

      <p id="d1e1606">This article is part of the special issue “Pan-Eurasian Experiment (PEEX)”. It is not associated with a conference.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e1612">This work was supported by the Ministry of Science and Technology of China
(no.: 2017YFC0210000), the Ministry of Science and Technology of China (no.:
2017YFC0210102), the National Research Program for key issues in air
pollution control (DQGG0101), the Beijing National Science Foundation of China
(8171002) and the Academy of Finland via the Center of Excellence in Atmospheric
Sciences.</p></ack><?xmltex \hack{\newpage}?><?xmltex \hack{\newpage}?><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e1618">This work was supported by the Ministry of Science and Technology of China (grant nos. 2017YFC0210000 and 2017YFC0210102),  the National Research Program for key issues in air pollution control (grant no. DQGG0101), the Beijing National Science Foundation of China (grant no. 8171002) and the Academy of Finland via Center of Excellence in Atmospheric Sciences.</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e1624">This paper was edited by Dominick Spracklen and reviewed by two anonymous referees.</p>
  </notes><ref-list>
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    <!--<article-title-html>Rapid formation of intense haze episodes via aerosol–boundary layer feedback in Beijing</article-title-html>
<abstract-html><p>Although much effort has been put into studying air pollution, our knowledge
of the mechanisms of frequently occurring intense haze episodes in China is
still limited. In this study, using 3 years of measurements of air
pollutants at three different height levels on a 325&thinsp;m Beijing
meteorology tower, we found that a positive aerosol–boundary layer feedback
mechanism existed at three vertical observation heights during intense haze
polluted periods within the mixing layer. This feedback was characterized by
a higher loading of PM<sub>2.5</sub> with a shallower mixing layer. Modelling
results indicated that the presence of PM<sub>2.5</sub> within the boundary layer led
to reduced surface temperature, relative humidity and mixing layer height
during an intensive haze episode. Measurements showed that the
aerosol–boundary layer feedback was related to the decrease in solar
radiation, turbulent kinetic energy and thereby suppression of the mixing
layer. The feedback mechanism can explain the rapid formation of intense
haze episodes to some extent, and we suggest that the detailed feedback
mechanism warrants further investigation from both model simulations and
field observations.</p></abstract-html>
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