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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-19-3287-2019</article-id><title-group><article-title>The two-way feedback mechanism between unfavorable <?xmltex \hack{\break}?> meteorological conditions and cumulative aerosol <?xmltex \hack{\break}?> pollution in various haze regions of China</article-title><alt-title>The two-way feedback mechanism in various haze regions of China </alt-title>
      </title-group><?xmltex \runningtitle{The two-way feedback mechanism in various haze regions of China }?><?xmltex \runningauthor{J.~Zhong et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Zhong</surname><given-names>Junting</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-4109-3405</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff3">
          <name><surname>Zhang</surname><given-names>Xiaoye</given-names></name>
          <email>xiaoye@cma.gov.cn</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Yaqiang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Wang</surname><given-names>Jizhi</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Shen</surname><given-names>Xiaojing</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff4">
          <name><surname>Zhang</surname><given-names>Hongsheng</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff5">
          <name><surname>Wang</surname><given-names>Tijian</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff6 aff7">
          <name><surname>Xie</surname><given-names>Zhouqing</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2 aff6 aff7">
          <name><surname>Liu</surname><given-names>Cheng</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-3759-9219</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff8">
          <name><surname>Zhang</surname><given-names>Hengde</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Zhao</surname><given-names>Tianliang</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Sun</surname><given-names>Junying</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff10">
          <name><surname>Fan</surname><given-names>Shaojia</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Gao</surname><given-names>Zhiqiu</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff9">
          <name><surname>Li</surname><given-names>Yubin</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff11">
          <name><surname>Wang</surname><given-names>Linlin</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, 100081, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Center for Excellence in Regional Atmospheric Environment, IUE, Chinese Academy of Sciences, Xiamen, 361021, China</institution>
        </aff>
        <aff id="aff4"><label>4</label><institution>Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, <?xmltex \hack{\break}?> School of Physics, Peking University, Beijing, 100081, China</institution>
        </aff>
        <aff id="aff5"><label>5</label><institution>School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China</institution>
        </aff>
        <aff id="aff6"><label>6</label><institution>Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, <?xmltex \hack{\break}?> Chinese Academy of Sciences, Hefei, 230031, China</institution>
        </aff>
        <aff id="aff7"><label>7</label><institution>School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China</institution>
        </aff>
        <aff id="aff8"><label>8</label><institution>National Meteorological Center, China Meteorological Administration, Beijing, 100081, China</institution>
        </aff>
        <aff id="aff9"><label>9</label><institution>School of Atmospheric Physics, Nanjing University of Information Science &amp; Technology, Nanjing, 210044, China</institution>
        </aff>
        <aff id="aff10"><label>10</label><institution>Department of Atmospheric Science, Sun Yat-sen University, Guangzhou, 510275, China</institution>
        </aff>
        <aff id="aff11"><label>11</label><institution>State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, <?xmltex \hack{\break}?> Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Xiaoye Zhang (xiaoye@cma.gov.cn)</corresp></author-notes><pub-date><day>13</day><month>March</month><year>2019</year></pub-date>
      
      <volume>19</volume>
      <issue>5</issue>
      <fpage>3287</fpage><lpage>3306</lpage>
      <history>
        <date date-type="received"><day>10</day><month>October</month><year>2018</year></date>
           <date date-type="rev-request"><day>5</day><month>November</month><year>2018</year></date>
           <date date-type="rev-recd"><day>19</day><month>February</month><year>2019</year></date>
           <date date-type="accepted"><day>21</day><month>February</month><year>2019</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2019 </copyright-statement>
        <copyright-year>2019</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>
    <?pagebreak page3288?><p id="d1e297">Accompanied by unfavorable meteorological conditions with stable
stratification in various haze regions of China, persistent heavy aerosol
pollution episodes (HPEs) lasting more than 3 consecutive days frequently
occur, particularly in winter. In the North China Plain (NCP), explosive
growth of fine particulate matter smaller than 2.5 <inline-formula><mml:math id="M1" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m in
diameter (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>), which occurs during some HPE<inline-formula><mml:math id="M3" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">S</mml:mi></mml:msub></mml:math></inline-formula>, is dominated by
a two-way feedback mechanism between more unfavorable
meteorological conditions and cumulative aerosol pollution. However, the
existence of a two-way feedback mechanism such as this in other key haze regions in China is
uncertain; these regions include the Guanzhong Plain (GZP), the Yangtze River
Delta (YRD) region, the Two Lakes Basin (TLB; a large outflow basin connected to Hubei Province and Hunan Province), the Pearl River Delta (PRD)
region, the Sichuan Basin (SB), and the Northeast China Plain (NeCP). In this
study, using surface PM<inline-formula><mml:math id="M4" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> and radiation observations, radiosonde
observations, and reanalysis data, we observed the existence of a two-way
feedback mechanism in the six abovementioned regions. In the SB, this two-way feedback
mechanism is weak due to the suppression of cloudy mid-upper layers. In the
more polluted NCP, the GZP, and the NeCP, the feedback is more striking than
that in the YRD, the TLB, and the PRD. In these regions, the feedback of
worsened meteorological conditions on PM<inline-formula><mml:math id="M5" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> explains 60 %–70 % of
the increase in 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> during the cumulative stages (CSs). For each
region, the low-level cooling bias becomes increasingly substantial with
increasing aerosol pollution and a closer distance to the ground surface.
With PM<inline-formula><mml:math id="M7" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentrations greater than 400 <inline-formula><mml:math id="M8" 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="M9" 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 near-ground bias exceeded <inline-formula><mml:math id="M10" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in Beijing and reached up to
approximately <inline-formula><mml:math id="M12" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M13" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in Xi'an; this result was caused by
accumulated aerosol mass to some extent. In addition to the increase in
PM<inline-formula><mml:math id="M14" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> caused by the two-way feedback, these regions also suffer from the
regional transport of pollutants, including inter-regional transport in the
GZP, trans-regional transport from the NCP to the YRD and the TLB, and
southwesterly transport in the NeCP.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p id="d1e438">In China, 94 % of the total population is distributed in eastern China
(Yang et al., 2016), in which aerosol pollution has drawn wide
attention. In the basins and plains in eastern China, aerosol pollution
episodes frequently occur in winter, and these episodes cause economic loss
and have adverse effects on human health (Bai et al., 2007; Matus et al.,
2012; Chen et al., 2013). For example, in January 2013, persistent heavy
aerosol episodes affected 600 million people over 1.4 million km<inline-formula><mml:math id="M15" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>
(<uri>http://www.infzm.com/content/95493</uri>, last access: 4 March 2019), which led to hundreds of
flight cancelations and an increase in the number of reported respiratory
disease cases (Ji et al., 2014). During the wintertime (i.e., December, January,
and February) from 2013 to 2017, more than 28 persistent heavy aerosol
pollution episodes (HPEs) that lasted for more than 3 consecutive days
occurred in Beijing; the peak value of particulate matter smaller than
2.5 <inline-formula><mml:math id="M16" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi></mml:mrow></mml:math></inline-formula>m in diameter (PM<inline-formula><mml:math id="M17" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>) ranged from <inline-formula><mml:math id="M18" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">200</mml:mn></mml:mrow></mml:math></inline-formula> to
<inline-formula><mml:math id="M19" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">800</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M20" 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="M21" 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>, with a mean duration longer than
5 days (Zhong et al., 2018a, 2019). The main cause of frequent
pollution episodes is the massive emissions of air pollutants produced by
intense living and industrial activities in the basins and plains (Q. Zhang
et al., 2009, 2012; Zhang et al., 2013). In addition to
pollutant emissions, the relatively closed terrains of basins and plains
limit the diffusion of aerosols and their precursors to the surrounding
areas (Su et al., 2004; Zhu et al., 2018). Under stable
meteorological conditions, aerosol pollution forms and further accumulates
(Zhang et al., 2013; Zhong et al., 2017).</p>
      <p id="d1e511">In winter, unfavorable meteorological conditions lead to aerosol pollution
formation, and after accumulating to some extent, aerosols change the
atmospheric structure by interacting with solar radiation (Boucher et
al., 2013b; Zhong et al., 2018b). The details of the two-way feedback
mechanism between unfavorable meteorological conditions and cumulative
aerosols in Beijing are as follows: (1) when upper zonal large-scale
circulations unfavorable for pollution dispersion occur, the boundary layer (BL)
height decreases from <inline-formula><mml:math id="M22" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">1500</mml:mn></mml:mrow></mml:math></inline-formula> m in clean stages to 700–800 m; under
the BL, the winds shift from northerly to southerly, which
transport pollutants from the south of Beijing (transport stages – TSs – in
Fig. 1). Abovementioned unfavorable meteorological conditions cause aerosol pollution
formation. (2) When the vertical aerosols have accumulated to a certain
degree, the dominant scattering aerosols will substantially backscatter
solar radiation, causing a reduction in the amount of solar radiation that
reaches the surface, which causes a near-ground cooling effect through
atmospheric circulation and vertical mixing (i.e., the cumulative sum of
hourly surface vertical and global radiant exposure decreased by 89 % and
56 %, respectively, from clean stages to cumulative stages – CSs; Fig. 1)
(Zhong et al., 2017, 2018b). With less solar radiation,
near-ground temperature subsequently decreases. (3) Under slight or calm
winds, the temperature reduction induces or reinforces an inversion that
further weakens turbulent diffusion and results in a lower BL height, which
in turn worsens aerosol pollution (during CSs in Fig. 1). (4) This condition
also decreases the near-ground saturation vapor pressure and suppresses
water vapor diffusion which, in turn, increases the relative humidity (RH); this increase in RH
further enhances aerosol hygroscopic growth and accelerates liquid-phase and
heterogeneous reactions to worsen aerosol pollution (Pilinis et al.,
1989; Ervens et al., 2011; Kuang et al., 2016; Zhong et al., 2018a, b).
This feedback effect of progressively unfavorable meteorological conditions aggravates
PM<inline-formula><mml:math id="M23" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> pollution (during CSs in Fig. 1) (Zhong et al., 2017).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><label>Figure 1</label><caption><p id="d1e535">Temporal variations in PM<inline-formula><mml:math id="M24" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation, and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> 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> mass concentration, <bold>(b)</bold> winds (vectors)
and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and
<bold>(e)</bold> direct radiant exposure (of the vertical surface to the direction
of solar radiation) and global radiant exposure. (Blue lines: HPEs; green
lines and orange boxes: TSs; red lines and white or red boxes: CSs; blue boxes: clean periods)
(adapted from Zhong et al., 2018b).</p></caption>
        <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f01.png"/>

      </fig>

      <p id="d1e578">The two-way feedback mechanism between unfavorable meteorological conditions
and cumulative aerosol pollution also appears in other cities in the North
China Plain, including Tangshan, Xingtai, Zhengzhou, and Nanyang (Liu et al., 2018).
Whether the two-way feedback mechanism exists in other
basins and plains in eastern China, which have weaker aerosol pollution than
that in the North China Plain, is unclear. If such feedback exists, its
magnitude requires further investigation. Currently, to the best of our
knowledge, studies on the existence, magnitude, and comparison of the
two-way feedback in these basins and plains are insufficient. Overall, we
lack a comprehensive understanding of the feedback mechanism in China.
Therefore, in this study we used surface 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> mass concentrations, radiosonde
observations of meteorological factors, the PLAM
(Parameter Linking Air-quality to Meteorological conditions/haze) index, and
ERA-Interim reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF)
to investigate the two-way feedback mechanism in the key
regions of populous eastern China (Yang et al., 2016), including the
Guanzhong Plain, the Yangtze River Delta, the Two Lakes Basin, the Pearl
River Delta, the Sichuan Basin, and the Northeast China Plain; these regions are
densely populated and economically developed areas that include massive
industrial pollution sources, agricultural pollution sources, motor vehicle
pollution sources, and domestic pollution sources. In the abovementioned regions,
heavy aerosol episodes often occur in the regional central cities that have
denser populations and stronger pollutant emissions, including Xi'an,
Nanjing, Shanghai, Wuhan, Guangzhou, Chengdu, and Shenyang. In these cities,
the impact of aerosol pollution episodes on the economy, society,
and health is far-reaching. Therefore, we focus on the feedback mechanism in
the above cities to represent the overall conditions in the five major haze
regions of China, which are as follows: (I) the Northeast China Plain; (II) the North China Plain (also known as Hua Bei
Plain) and the Guanzhong Plain in<?pagebreak page3289?> northern China; (III) eastern China –
comprised mainly of the Yangtze River Delta area; (V) southern China – including most areas of
Guangdong and the Pearl River Delta area; and (IV) the Sichuan Basin in
southwestern China (X. Y. Zhang et al., 2012) (Fig. 1).</p>
</sec>
<sec id="Ch1.S2">
  <title>Materials and methods</title>
<sec id="Ch1.S2.SS1">
  <?xmltex \opttitle{PM${}_{{2.5}}$ observations}?><title>PM<inline-formula><mml:math id="M27" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> observations</title>
      <p id="d1e611">Since January 2013, the Ministry of Environmental Protection has been
monitoring the PM<inline-formula><mml:math id="M28" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentrations in real time at over
1000 environmental monitoring stations established in different regions of China.
In this study, we used the hourly PM<inline-formula><mml:math id="M29" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentrations provided by
the Ministry of Environmental Protection from 1 December 2016 to 10 January 2017
and the respective averaged PM<inline-formula><mml:math id="M30" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentrations of all
the urban stations in Xi'an, Yuncheng, Shenyang, Chengdu, Wuhan, Nanjing,
Shanghai, Jinan, Guangzhou, and Qingyuan. The in situ monitoring data of the
hourly concentrations of PM<inline-formula><mml:math id="M31" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, PM<inline-formula><mml:math id="M32" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">10</mml:mn></mml:msub></mml:math></inline-formula>, CO, <inline-formula><mml:math id="M33" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id="M34" 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
<inline-formula><mml:math id="M35" 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> were acquired from the national air quality real-time publishing
platform (<uri>http://106.37.208.233:20035</uri>, last access: 4 March 2019).</p>
      <p id="d1e696">We also used an unmanned aerial vehicle to observe the PM<inline-formula><mml:math id="M36" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration
at different heights at 1 km intervals every 3 h in Nanjing from 3 to 4 December 2017. The data were obtained with the CEEWA X8 UAV platform (Nanjing
CEEWA Intelligent Technology Co., Ltd., Nanjing, China), a six-rotor
industrial UAV carrying a highly reliable, triple-redundant FC-IU3 flight
control system (Zhou et al., 2018). A multiparameter atmospheric environment detector was equipped on the platform, which was
developed by Shenzhen Tengwei Measurement and Control Technology Co., Ltd.,
Shenzhen, China (Zhou et al., 2018). The experiment was performed
on the Xianlin campus of Nanjing University. This campus is located in the
eastern suburb of Nanjing and is surrounded by farmland, residential areas, and
small patches of forest and chemical plants. The experimental site was located
on a playground which was not surrounded by any tall structures.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Meteorological radiosonde observations</title>
      <p id="d1e714">In China, 120 stations observe vertical meteorological factors
using L-band sounding radars. Their accurately<?pagebreak page3290?> positioned radar systems
collect reliable meteorological data each second; thus, these data have high
spatial and temporal resolutions (Tao, 2006). In this study, we used the
L-band sounding radar data from the meteorological stations in Xi'an,
Shenyang, Chengdu, Wuhan, Nanjing, Shanghai, and Qingyuan; these stations
observe several meteorological factors, including wind, temperature, and RH,
twice each day at 08:00 and 20:00 BJT (Beijing time) from 1 December 2016 to 10 January 2017.
The meteorological factors were analyzed in detail below a
height of 3 km. The heights from the surface to 1 km, from 1 to 2 km, and
from 2 to 3 km are termed low-level, mid-level, and upper-level, respectively.
In addition, due to the lack of meteorological
radiosonde observations in Guangzhou, we supplemented related observations
from an adjacent city, Qingyuan.</p><?xmltex \hack{\vspace*{2mm}}?>
</sec>
<sec id="Ch1.S2.SS3">
  <title>Surface meteorological data</title>
      <p id="d1e724"><?xmltex \hack{\vspace*{2mm}}?>Since 2001, national weather stations have been conducting automatic hourly
observations. Starting in 2001, some of the stations began to record observations
at 5 or 10 min intervals. This study used the hourly meteorological
observation data, including temperature, pressure, RH, wind, and visibility
from the national automatic weather stations (AWS) provided by the National
Meteorological Information Center of China Meteorological Administration (NMICMA).
The time period of the data selected is from 1 December 2016 to 10 January 2017.</p>
      <p id="d1e728">We also used an hourly radiant exposure data set of national meteorological
radiation factors (V2.0) provided by the NMICMA. This data set contains
104 radiation stations, including the following: primary stations with global, direct,
scattered, reflected, and net radiation; secondary stations with global and
net radiation; and tertiary stations with only global radiation. These
radiation stations have recorded hourly basic radiant exposure data and the
corresponding station information (i.e., latitude, longitude, and altitude)
since 1993. In this study, we used the global, direct, and net radiant
exposure from 1 December 2016 to 10 January 2017.</p><?xmltex \hack{\vspace*{2mm}}?>
</sec>
<sec id="Ch1.S2.SS4">
  <title>PLAM data</title>
      <p id="d1e738"><?xmltex \hack{\vspace*{2mm}}?>Based on the definition and calculation formula of PLAM
(Parameter Linking Air-quality to Meteorological conditions/haze; X. Y. Zhang et al.,
2009; Wang et al., 2012, 2013; Zhang et al., 2015), we obtained
the PLAM index values in Xi'an, Nanjing, Wuhan, Qingyuan, Chengdu, and Shenyang using
surface meteorological factors. PLAM includes two separate major factors:
(1) the initial meteorological conditions <inline-formula><mml:math id="M37" display="inline"><mml:mrow><mml:mi mathvariant="italic">α</mml:mi><mml:mo>(</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> associated with the
atmospheric condensation processes, and (2) an effective dynamic parameter
associated with the initial contribution of air pollution <inline-formula><mml:math id="M38" display="inline"><mml:mrow><mml:mi mathvariant="italic">β</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>c</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>:

                <disp-formula id="Ch1.E1" content-type="numbered"><mml:math id="M39" display="block"><mml:mstyle displaystyle="true" class="stylechange"/><mml:mrow><mml:mstyle displaystyle="true" class="stylechange"/><mml:mi mathvariant="normal">PLAM</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="italic">α</mml:mi><mml:mo>(</mml:mo><mml:mi>m</mml:mi><mml:mo>)</mml:mo><mml:mo>×</mml:mo><mml:msup><mml:mi mathvariant="italic">β</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:mo>(</mml:mo><mml:mi>c</mml:mi><mml:mo>)</mml:mo><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>

          This calculation mainly indicates regional atmospheric stability and air
condensation ability. The details of the calculation have been presented in
previous studies (Wang et al., 2012, 2013).</p>
</sec>
<sec id="Ch1.S2.SS5">
  <title>ECMWF ERA-Interim data</title>
      <p id="d1e816">ERA-Interim is the ECMWF's latest global atmospheric reanalysis, which extends
back to 1979 and continuously updates in real time (Dee et al., 2011). It
is produced with a four-dimensional variational data assimilation scheme and
advances forward in time using 12 h analysis cycles (Thépaut et
al., 1996; Dee et al., 2011). Before assimilation, all data are subject to
gross, redundancy, and background quality controls, which results in a large
drop between the total number of data available and the number of data used
in the assimilation. The mean daily usage rate of radiosondes is no more
than 50 % over the entire period (Poli et al., 2010). In
addition, although the effect of aerosols on radiative transfer in the
atmosphere is modeled based on prescribed climatological aerosol
distributions (Dee et al., 2011), it does not consider the two-way
feedback mechanism between the cumulated aerosol pollution and deteriorating
meteorological conditions (Simmons, 2006). Therefore, the magnitude of
the feedback mechanism could be statistically reflected by the gaps between
the ERA-Interim reanalysis and the meteorological radiosonde observations.
Therefore, the disparities have been used to present the observational evidence of
aerosol–planetary boundary layer interactions in Beijing (Ding et al., 2016; Ding et al., 2016).</p>
      <p id="d1e819">In this study, we used ERA-Interim data with a horizontal resolution of
0.125<inline-formula><mml:math id="M40" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> <inline-formula><mml:math id="M41" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 0.125<inline-formula><mml:math id="M42" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>. Its mandatory pressure levels
include 1000, 975, 950, 925, 900, 875, 850, 825, 800, 775, 750, and 700 hPa.
According to these pressure layers, we interpolated the radiosonde
observations and calculated the vertical temperature differences between the
ERA-Interim reanalysis and the interpolated sounding data at 20:00 BJT.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><label>Figure 2</label><caption><p id="d1e849">The key haze regions in China with similar declines in visibility including the North China Plain (NCP), the Guanzhong Plain (GZP), the Yangtze River Delta (YRD) region, the Two Lakes Basin (TLB), the Pearl River Delta (PRD) region, the Sichuan Basin (SB), and the Northeast China Plain (NeCP).
Grey dots represent the locations of radiosonde stations.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f02.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><label>Figure 3</label><caption><p id="d1e861">National distribution of mean PM<inline-formula><mml:math id="M43" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration from
1 December 2016 to 10 January 2017. Grey dots represent the locations of radiosonde stations.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f03.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results and discussions</title>
      <p id="d1e886">Based on the consistent variation in visibility trends, China is classified
into nine typical regions (X. Y. Zhang et al., 2012). Five of these regions have experienced striking declines in visibility in recent
decades, including (1) the North China Plain and the Guanzhong Plain in
northern China; (2) the Yangtze River Delta region and the Two Lakes Basin
along the middle and lower reaches of the Yangtze River; (3) the Pearl River
Delta region in southern China; (4) the Sichuan Basin in southwestern China;
(5) and the Northeast China Plain (Fig. 2). The areas where declines in
visibility are observed coincide with the basins and plains in eastern China, as these basins
and plains are densely populated and topographically enclosed; additionally,
these areas emit and produce massive air pollutants, including primary
aerosols and<?pagebreak page3291?> secondary aerosols from gas-to-particle conversion. These
aerosols accumulate locally to continuously reduce visibility. By comparing
the mean PM<inline-formula><mml:math id="M44" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration from 1 December 2016 with that of
10 January 2017 in the five regions that experienced declines in visibility
(Fig. 3), we found that the heaviest aerosol pollution occurred in the North
China Plain, and it was followed by the Guanzhong Plain. The areas with the
next highest aerosol pollution were the Sichuan Basin and the Northeast
China Plain. The Two Lakes Basin and the Yangtze River Delta experienced
less aerosol pollution, whereas the Pearl River Delta displayed the least aerosol pollution.</p>
<sec id="Ch1.S3.SS1">
  <title>Striking two-way feedback in the polluted Guanzhong Plain with inter-regional pollution transport</title>
      <p id="d1e903">To the north of the Loess Plateau and the south of the Qinling Mountains,
the Guanzhong Plain has a narrow and closed terrain (Fig. 2), and its
climatic and meteorological conditions are distinctive from those of the
surrounding areas. Compared with the plateau to the north, the Guanzhong
Plain is less affected by northerly cold, clean winds, and these
conditions favor the accumulation of pollutants. However, because the Loess
Plateau is lower in elevation than the Hengduan Mountains and the Daba
Mountains located to the northwest of the Sichuan Basin, the barrier effect
of the plateau on the northerly cold air is weaker than that of the
abovementioned mountains (Figs. 4b, 12b). Because the North China<?pagebreak page3292?> Plain is
bordered to the west by the Taihang and the Lüliang Mountains
(Fig. 2), the Guanzhong Plain is rarely affected by pollutant transport from
the North China Plain; however, air pollution is highly correlated among the
different cities in the Guanzhong Plain. To the west of this plain, Xi'an
lies north of the Wei River and the Loess Plateau and south of the Qinling
Mountains (Fig. 2). Due to its enclosed topography, Xi'an frequently
experiences heavy urban aerosol pollution.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><label>Figure 4</label><caption><p id="d1e908">Temporal variations in PM<inline-formula><mml:math id="M45" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation, and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> PM<inline-formula><mml:math id="M46" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration, <bold>(b)</bold> winds (vectors) and wind velocity,
<bold>(c)</bold> temperature, <bold>(d)</bold> RH, and <bold>(e)</bold> global radiant exposure.
(Blue lines: HPEs; light blue lined: PEs; white or red boxes: CSs; and brown boxes:
water vapor transport.)</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f04.png"/>

        </fig>

      <p id="d1e951">From 1 December 2016 to 10 January 2017, two HPEs occurred in Xi'an and
persisted for more than 7 days with peak mass concentrations greater than
400 <inline-formula><mml:math id="M47" 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="M48" 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> (Fig. 4a, dark blue lines). During HPE<inline-formula><mml:math id="M49" display="inline"><mml:msub><mml:mi/><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msub></mml:math></inline-formula>, we
observed a striking two-way feedback mechanism between the deteriorating weather
conditions and the cumulated aerosol pollution (Fig. 4, red and white
boxes). When the near-ground PM<inline-formula><mml:math id="M50" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> accumulates to a certain extent, the
particles scatter more solar radiation back to space, which substantially
reduces the surface radiation (Fig. 4e, red boxes) and consequently
lowers the near-surface temperature (Fig. 4c, white boxes). Under slight
or calm winds (Fig. 4b, red boxes), the temperature reduction induces or
reinforces inversions, which further weaken turbulent diffusion and suppress
the diffusion of water vapor and pollutants (Zhong et al., 2017,
2018a); these conditions also decrease the near-ground saturation vapor
pressure to increase the RH (Fig. 4d, red boxes), which further enhances
aerosol hygroscopic growth and accelerates liquid-phase and heterogeneous
reactions (Cheng et al., 2016; Fang et al., 2016; Tie et al., 2017). This
type of two-way feedback mechanism leads to declining meteorological
conditions and elevated 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> mass concentrations.</p>
      <p id="d1e1006">To quantify the magnitude of the two-way feedback during TSs and CSs in
polluted Beijing and Xi'an, we obtained the air temperature difference
between the radiosonde observations at 20:00 BJT affected by the two-way
feedback (where the temperature profiles were more affected by aerosols
blocking in solar radiation transfer compared with observations at 08:00 BJT)
and the ERA-Interim reanalysis data without the feedback. We found that the
temperature profile was modified by aerosols during both the TSs and the CSs
in Beijing and Xi'an (Fig. 5a, b). However, a comparison of
aerosol-induced temperature modification in the TSs and the CSs indicates
that the lower cooling bias was more striking in the CSs, and that this bias also became
increasingly substantial at distances closer to the surface.
From TSs to CSs, the negative temperature difference at 1000 hPa increased from
<inline-formula><mml:math id="M52" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.8</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4.3</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M54" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and from <inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.3</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M56" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2.7</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
in Beijing and Xi'an, respectively. Using the near-ground
temperature reduction (1000 hPa) as an index to evaluate the magnitude of
the two-way feedback from TSs to CSs, we found that during TSs the
aerosol-induced cooling bias was 18.6 % and 48.7 % of the difference
during CSs in Beijing and Xi'an, respectively. This is expected because
aerosol pollution worsened from the TSs to the CSs  with increasing radiative
cooling effects. Moreover, although relatively strong winds in the TSs were
conducive to pollution transport, they were unfavorable for the formation
and maintenance of stable stratification, in which aerosol
self-induced pollution deterioration frequently occurred.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5"><label>Figure 5</label><caption><p id="d1e1071">Vertical mean temperature difference between sounding observations and
ERA-Interim reanalysis data during TSs and CSs from 1 December 2016 to
10 January 2017 in <bold>(a)</bold> Beijing and <bold>(b)</bold> Xi'an.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f05.png"/>

        </fig>

      <p id="d1e1086">During HPE<inline-formula><mml:math id="M58" display="inline"><mml:msub><mml:mi/><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>-</mml:mo><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msub></mml:math></inline-formula>, we also observed an increase in the PM<inline-formula><mml:math id="M59" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass
concentration caused by pollutant transport. The aerosol pollution in Xi'an
might have been aggravated by the transport of pollutants from the eastern polluted
plain area with heavily polluted cities, including Yuncheng and Linfen. To
reveal the effects of air pollutant transport from the eastern plain on the
aerosol pollution in Xi'an, we compared the trends in the variation of the
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> mass concentrations in Xi'an and Yuncheng under lower
northwesterly winds (Fig. 4a, b). We found that during TSs (Fig. 4,
orange boxes), low-level northwesterly winds transported pollutants
below the BL which maintained or aggravated the aerosol pollution in Xi'an when
Yuncheng was heavily polluted; however, when the air quality in Yuncheng was good,
the aerosol pollution in Xi'an was lighter or even eliminated.</p>
      <p id="d1e1121">In addition to the scavenging effect of clean northwesterly winds on aerosol
pollution, pollution elimination mainly depends on lower strong
northwesterly winds and mid-upper level southerly winds. Because the Loess
Plateau north of Xi'an is sparsely populated with rare air pollutant
emissions, lower strong and clean northwesterly winds would blow
aerosol pollutants away in Xi'an, causing a subsequent rapid improvement in the
air quality (Fig. 4a, b). As the mid-upper level southerly winds
transport water vapor to Xi'an from the area south of the city, the mid-upper
(or whole-layer) RH level is considerably enhanced (i.e., greater than
96 %; Fig. 4b, d – brown boxes), which causes the 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> to enter
the fog-cloud phase and possibly produces precipitation that eliminates
pollutants through wet removal (Fig. 4d, blue dots represent precipitation).</p>
</sec>
<sec id="Ch1.S3.SS2">
  <title>Affected by trans-regional pollution transport from the North China Plain, the Yangtze River Delta region subsequently experiences the two-way feedback</title>
      <p id="d1e1139">Located in the lower reaches of the Yangtze River, the Yangtze River Delta
is a triangle-shaped metropolitan region. It covers an area of
211 700 km<inline-formula><mml:math id="M62" 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> and is home to more than 150 million people as of 2014
(<uri>http://www.ndrc.gov.cn/zcfb/zcfbghwb/201606/t20160603_806390.html</uri>, last access: 4 March 2019).
The urban buildup in this area has given rise to what may be
the largest concentration of adjacent metropolitan areas in the world. The
Yangtze River Delta has a marine monsoon subtropical climate with cool,
dry winters. Situated in the Yangtze River Delta, Nanjing is the second
largest city in the eastern China region. The southern, northern, and eastern sides of
the city are surrounded by the Ningzheng Ridges (Fig. 2), whereas the Yangtze
River flows west of the city and along part of the northern margin.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><label>Figure 6</label><caption><p id="d1e1159">Temporal variations in PM<inline-formula><mml:math id="M63" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation, and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> 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> mass concentration, <bold>(b)</bold> winds (vectors)
and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and <bold>(e)</bold> global radiant exposure.
(Blue lines: HPEs; light blue lines: PEs; white or red boxes: CSs; and blue dots: precipitation.)</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f06.png"/>

        </fig>

      <p id="d1e1202">From 1 December 2016 to 10 January 2017, four aerosol pollution episodes
occurred in Nanjing (Fig. 6a, blue boxes).<?pagebreak page3293?> One of these episodes lasted
for less than 3 days and exhibited light pollution, whereas the other three episodes
persisted for more than 5 days and had peak mass concentrations greater than
150 <inline-formula><mml:math id="M65" 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="M66" 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>; thus, these three episodes were termed HPEs
(Fig. 6a). During these three HPEs, although 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> mass concentration
was much lower than that in Beijing, the aerosol pollution formation was
similar to that in Beijing, including earlier TSs and later CSs (Zhong
et al., 2017, 2018a). During the TSs in the HPEs, strong
northerly winds transported aerosol pollutants from the polluted North China
Plain to the Yangtze River Delta region below and above the BL (i.e.,
long-distance pollution transport), which induced a striking increase in 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> mass concentration in Nanjing and a reduction in the 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>
mass concentration in Jinan, a regional center city representative of the
pollution conditions in the NCP (Fig. 6a, b). To some extent, based on
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, the two-way feedback mechanism was activated during the
CSs, in which we observed surface radiation reductions, near-surface
inversions, low-layer RH enhancement, and increased 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> mass
concentrations under slight winds (Fig. 6). Due to the lighter aerosol
pollution in Nanjing, the two-way feedback mechanism was weaker than that in
Beijing (Figs. 1, 6a). In addition, the mechanism might have been weakened by
relatively strong lower winds (compared with those in Beijing) (Figs. 1, 6b),
which are unfavorable for the accumulation of aerosols.</p>
      <?pagebreak page3294?><p id="d1e1271">To reveal the regional pollutant transport patterns from the North China
Plain to the Yangtze River Delta region, we calculated the concentration
difference in the PM<inline-formula><mml:math id="M72" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass between the start time and the end time of
the TSs in HPE<inline-formula><mml:math id="M73" display="inline"><mml:msub><mml:mi/><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">2</mml:mn><mml:mo>,</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:msub></mml:math></inline-formula> (Fig. 7). We found that the southern area of the
North China Plain experienced a substantial reduction in its 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> mass
concentration, whereas an increase occurred in the middle and lower reaches of
the Yangtze River, including the Two Lakes Basin and the Yangtze River Delta
region; these results indicate the process of regional pollutant transport
from northern China to eastern China under strong northwesterly
winds. In the winter of 2017, we also observed this pollution transport
(Fig. 6, orange boxes). As shown in Fig. 8, we observed mid-level pollutant
transport at the beginning of the TS, after which near-surface PM<inline-formula><mml:math id="M75" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
reaches the highest concentration with downward mixing. Subsequently,
another northerly pollutant transport reached middle layers over Nanjing.
Afterward, persistent northerly winds blew pollutants away (Fig. 8, purple
boxes). In addition to the “blowing effect” of persistent northerly winds,
eliminating pollution in Nanjing mainly depends on strong southeasterly
winds, which transport warm, humid, clean air from the Yellow Sea and
the East China Sea; this air also blows the aerosol pollutants in Nanjing
away (Fig. 6b–d). In addition, transported water vapor increases the RH
(Fig. 6b, d), which causes the PM<inline-formula><mml:math id="M76" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> to enter the fog-cloud phase
and possibly produces precipitation that eliminates pollutants through wet
removal (Fig. 6d, blue dots represent precipitation).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7" specific-use="star"><label>Figure 7</label><caption><p id="d1e1331">The distribution of the concentration differences in 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> mass
between the start time and the end time (the end is subtracted from the start)
of the TSs in Fig. 4: <bold>(a)</bold> TS<inline-formula><mml:math id="M78" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:math></inline-formula> in HPE<inline-formula><mml:math id="M79" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:math></inline-formula>; <bold>(b)</bold> TS<inline-formula><mml:math id="M80" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>
in HPE<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>; <bold>(c)</bold> TS in HPE<inline-formula><mml:math id="M82" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>; and <bold>(d)</bold> TS in HPE<inline-formula><mml:math id="M83" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula>.</p></caption>
          <?xmltex \igopts{width=455.244094pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f07.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8" specific-use="star"><label>Figure 8</label><caption><p id="d1e1418">Temporal variations in PM<inline-formula><mml:math id="M84" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> and vertical distributions of
meteorological factors from 1 to 9 December 2017. <bold>(a)</bold> PM<inline-formula><mml:math id="M85" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass
concentration, <bold>(b)</bold> winds (vectors) and wind velocity, <bold>(c)</bold> temperature, and <bold>(d)</bold> RH.
(Orange boxes: TSs; purple boxes: clean periods.)</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f08.png"/>

        </fig>

      <p id="d1e1458">Consistent with the results observed in Nanjing, Shanghai also experienced
long-distance pollution transport below and above the BL under northwesterly
winds (Fig. 9a, b – orange boxes). After 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> had accumulated to some
extent, we observed a two-way feedback mechanism, including reduced
radiation, near-surface inversions, RH enhancement in the lower parts of BL,
and an increase in the 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> mass concentration under slight or calm winds
(Fig. 9a–e, red and white boxes); however, the magnitude of the
feedback was weaker than that observed in Nanjing (Fig. 6). Because Shanghai
is closer to the sea than Nanjing, it is more susceptible to warm, humid
southeasterly winds from the sea, which carry more water vapor to Shanghai
than to Nanjing (Figs. 6 and 9b, d).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9" specific-use="star"><label>Figure 9</label><caption><p id="d1e1481">Temporal variations in PM<inline-formula><mml:math id="M88" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation, and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> PM<inline-formula><mml:math id="M89" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration, <bold>(b)</bold> winds (vectors)
and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and <bold>(e)</bold> direct radiant exposure
(of the vertical surface to the direction of solar radiation) and global radiant
exposure. (White or red boxes: CSs; orange boxes: TSs; and blue dots: precipitation).</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f09.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS3">
  <title>The two-way feedback in the Two Lakes Basin with aerosol pollution worsened by transport</title>
      <p id="d1e1530">The Two Lakes Basin is in the middle reaches of the Yangtze River. With the
Sichuan Basin bordered to the northwest by the Daba Mountains (Fig. 2), the
Two Lakes Basin is rarely affected by pollutant transport from polluted
cities in the<?pagebreak page3295?> Sichuan Basin. The north side of the Two Lakes Basin is connected
to the North China Plain via the “Suizhou corridor” and the Nanyang Basin
(Fig. 2); thus, the Two Lakes Basin is vulnerable to pollution transport
from the North China Plain, which experiences the heaviest aerosol pollution
in China (Fig. 3). As a large exorheic basin surrounded by low ridges or
mountains, the Two Lakes Basin more frequently exchanges air masses with its
surroundings, with wind speeds much higher than those in the Sichuan Basin.
Situated in the eastern Two Lakes Basin, Wuhan is the most populous city in
central China. The Yangtze and Han rivers wind through this city, which has
a southern hilly and central flat terrain (Fig. 2).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F10" specific-use="star"><label>Figure 10</label><caption><p id="d1e1535">Temporal variations in 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>, surface radiation, and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> 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> mass concentration, <bold>(b)</bold> winds
(vectors) and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and
<bold>(e)</bold> direct radiant exposure (of the vertical surface to the direction
of solar radiation) and global radiant exposure. (Blue lines: HPEs; light blue
lines: PEs; white or red boxes: CSs; orange boxes: TSs; brown boxes: water vapor
transport; and blue dots: precipitation.)</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f10.png"/>

        </fig>

      <p id="d1e1578">From 1 December 2016 to 10 January 2017, four aerosol pollution episodes
occurred in Wuhan (Fig. 10a, blue boxes). Three of these episodes lasted
longer than 5 days and had peak mass concentrations greater than
150 <inline-formula><mml:math id="M92" 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="M93" 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>, and are termed HPEs (Fig. 10a). During these three HPEs, we
observed a two-way feedback mechanism (red boxes in Fig. 10), including
surface radiation reductions, near-surface inversions, low-level RH
enhancement, and increases in 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> mass concentrations under slight or
calm winds. Similar to the conditions observed in Nanjing, Wuhan
experienced lighter aerosol pollution than Beijing (Figs. 1, 10a); thus,
the two-way feedback mechanism is weaker than that observed in Beijing.</p>
      <p id="d1e1610">Figure 7 shows the regional pollutant transport from the North China Plain
to the Two Lakes Basin, which also aggravates the 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> pollution in
Wuhan. As shown in the orange boxes in Fig. 10, the lower northerly winds
transported pollutants from the north of Wuhan to below Wuhan and sometimes
above the BL, which resulted in increasing 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> mass concentrations.
Therefore, favorable northerly winds establish a pollution linkage between
the North China Plain and the middle and lower reaches of the Yangtze River
(including the Yangtze River Delta and the Two Lakes Basin), which have low
and flat terrains (Fig. 2). However, if the northerly winds are persistent
and strong enough, they blow the aerosol pollutants out of the North
China Plain entirely and then transport clean, cold winds to Wuhan; under
these conditions, the PM<inline-formula><mml:math id="M97" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration first increases and then
decreases dramatically. This phenomenon was observed from 12 to 14 December 2016
and is shown in Fig. 10.</p>
      <p id="d1e1641">In addition to the blowing effect of the strong, persistent northerly winds,
clearing the pollution in Wuhan mainly depends on the mid-upper level
southerly winds, particularly the southwesterly winds, which transport water
vapor to Wuhan from the south, substantially enhancing the RH (over 96 %)
(Fig. 10b, d, brown boxes); these conditions cause the PM<inline-formula><mml:math id="M98" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> to
enter the fog-cloud phase and often produce precipitation that eliminates
pollutants through wet removal (Fig. 10d, blue dots represent precipitation).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F11" specific-use="star"><label>Figure 11</label><caption><p id="d1e1655">Temporal variations in PM<inline-formula><mml:math id="M99" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation, and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> PM<inline-formula><mml:math id="M100" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration, <bold>(b)</bold> winds (vectors)
and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and <bold>(e)</bold> direct radiant exposure
(of the vertical surface to the direction of solar radiation) and global radiant
exposure. (Blue lines: HPEs; white or red boxes: CSs; purple boxes: clearing; brown
boxes: water vapor transport; and blue dots: precipitation.)</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f11.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS4">
  <title>The two-way feedback in the less polluted Pearl River Delta region, which is also humidified by upper southerly winds and purified by lower clean, cold northeasterly winds</title>
      <p id="d1e1705">Located in southeastern Guangdong Province, the Pearl River
Delta is one of the most populous and densely urbanized regions in the
world. This low-lying area is surrounded by the Pearl River estuary, where
the Dong, Bei, and Xi rivers converge to flow into the South
China Sea. With the South China Sea to its south, the Pearl River Delta
region is often influenced by southerly sea winds; however, with the
mountainous area in northern Guangdong to the north (Fig. 2), the<?pagebreak page3296?> Pearl
River Delta region is less affected by northerly cold, clean winds.
Situated at the heart of the Pearl River Delta region (Fig. 2), Guangzhou is
the most populous city of Guangdong Province. However, due to the lack of a
meteorological radiosonde station in Guangzhou, we had to use the sounding
observations from Qingyuan, a city with similar PM<inline-formula><mml:math id="M101" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> variation trends
(Fig. 9a); Qingyuan is located approximately 60 km north of Guangzhou.</p>
      <p id="d1e1717">From 1 December 2016 to 10 January 2017, the PM<inline-formula><mml:math id="M102" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass
concentration in Guangzhou and Qingyuan is <inline-formula><mml:math id="M103" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">50</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M104" 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="M105" 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>,
which is much lower than that in Xi'an, Nanjing, Wuhan, Chengdu,
and Shenyang. During this period, only one HPE occurred, and it lasted for
more than 8 days with a peak mass concentration of approximately
150 <inline-formula><mml:math id="M106" 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="M107" 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> (Fig. 11, blue line). During this episode, we observed surface
radiation reductions, near-surface inversions, low-level RH enhancement, and
increases in the PM<inline-formula><mml:math id="M108" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration under slight or calm winds
(Fig. 11, red/white boxes below the blue line), which suggest that a two-way
feedback mechanism exists in the region. Except for this episode, we found
that the PM<inline-formula><mml:math id="M109" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration increased during slight or calm winds
but was still below the threshold (Fig. 11, the red boxes before 1 January 2017)
(Zhong et al., 2019); thus, no inversion or increased RH occurred because the
two-way feedback mechanism was not effectively activated.</p>
      <p id="d1e1798">Clearing pollution from Qingyuan depends on the lower strong northeasterly
winds, which transport dry, cold, clean air to decrease the temperature and
RH and blow aerosol pollutants away from Qingyuan (Fig. 11b, d, purple
boxes). In<?pagebreak page3297?> addition to the blowing effect of the cold northeasterly winds,
the aerosol pollution in Qingyuan is also affected by the mid-upper level
sea flows, which enhance the atmospheric RH and cause the PM<inline-formula><mml:math id="M110" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> to
enter the fog-cloud phase in addition to possibly producing precipitation that eliminates
pollutants through wet removal (Fig. 11d, blue dots represent precipitation).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F12" specific-use="star"><label>Figure 12</label><caption><p id="d1e1812">Temporal variations in PM<inline-formula><mml:math id="M111" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> PM<inline-formula><mml:math id="M112" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration, <bold>(b)</bold> winds
(vectors) and wind velocity, <bold>(c)</bold> temperature, and <bold>(d)</bold> RH.
(Blue lines: HPEs; white or red boxes: CSs; brown boxes: warm air flow or inversions;
and blue dots: precipitation.)</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f12.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS5">
  <title>The two-way feedback is weakened by cloudy mid-upper layers in the humid Sichuan Basin – this area is capped by upper-level inversions caused by air moving east across the Tibetan Plateau</title>
      <p id="d1e1858">Located in the upper reaches of the Yangtze River in southwestern China, the
Sichuan Basin is a lowland region surrounded by mountains on all sides
(Fig. 2). Abutting the eastern edge of the Tibetan Plateau to the west and
northwest and the Daba Mountains and the Wu Mountains to the east and
northeast, respectively (Fig. 2), the Sichuan Basin is rarely affected by
cold northerly winds, which are blocked by the high mountains. On the
southern and southeastern sides, the Sichuan Basin is flanked by the lower
Yungui Plateau (Fig. 2), which is frequently affected by warm, humid
southwesterly and southeasterly airflows from the Bay of Bengal and the
southeastern sea. Transported water vapor from the south is blocked by the
tall northern mountains and then accumulates in the Sichuan Basin. Located
at the western edge of the Sichuan Basin, Chengdu is surrounded by the
highlands to the south, the high and steep Longmen Mountains to the
northwest, the Qionglai Mountains to the west, and the low Longquan
Mountains to the east. The enclosed topographical features lead to a lower
wind speed and a higher RH in Chengdu than in other parts of the Sichuan Basin.</p>
      <p id="d1e1861">From 1 December 2016 to 10 January 2017, three HPEs occurred in Chengdu
(Fig. 12, blue boxes), and these episodes lasted for more than 10 days and
had peak mass concentrations greater than 200 <inline-formula><mml:math id="M113" 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="M114" 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>
(Fig. 12a). During these three episodes, we observed thick mid-upper level
fog/cloud above Chengdu (Fig. 12d), which was blocked by the surrounding
mountains and upper-level inversions. The mid-upper level cloud competes
with the near-surface aerosols for solar radiation, i.e., as more solar
radiation is reflected by the mid-upper layer cloud, the<?pagebreak page3298?> near-surface
aerosols receive less solar radiation. Therefore, with cloudy mid-upper
layers, more solar radiation is reflected to cool the atmosphere below the
clouds, and this condition suppresses the two-way feedback mechanism between
the unfavorable weather conditions and the near-surface aerosols.
Consequently, the two-way feedback was weak and nearly no near-ground
temperature inversion was observed (Fig. 12c). Despite the lack of a
two-way feedback mechanism to aggravate aerosol pollution, the increase in
the PM<inline-formula><mml:math id="M115" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration is still dominated by slight or calm winds
under stable stratification(Fig. 12, red boxes). Comparing the
RH variations in the two processes of increasing PM<inline-formula><mml:math id="M116" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> (Fig. 12 red boxes)
during the HPE from 26 December 2016 to 6 January 2017, we found that the
PM<inline-formula><mml:math id="M117" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration increases in connection with lower RH.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F13"><label>Figure 13</label><caption><p id="d1e1913">Vertical section of mean air temperature in December 2016 at 30.67<inline-formula><mml:math id="M118" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f13.png"/>

        </fig>

      <p id="d1e1931">In addition to the near-surface weak winds, persistent aerosol pollution is
a result of temperature inversions caused by the warm southwest advection
(Fig. 12b, c, brown boxes). The ground of the Qinghai–Tibet Plateau is a
heat source throughout the year (Ye and Gao, 1979); thus, it heats the
near-surface ambient air (Fig. 13). When the relatively warm air moves east
across the Tibet Plateau under southwesterly winds, it forms an
inversion above the basin (Fig. 12c, brown boxes), which caps the
convective layer and induces the accumulation of aerosols and water vapor.</p>
      <?pagebreak page3299?><p id="d1e1935">Effective pollution clearing rarely occurs in Chengdu because the Sichuan
Basin is less affected by the cold, clean northerly winds as a result of the
surrounding high northern mountains. However, as soon as aerosol pollutants
and water vapor are cleared, aerosol pollution forms again due to more
longwave radiation lost from the ground. For example, during the 4–7 December 2016 period,
the fog/cloud dissipated, and the PM<inline-formula><mml:math id="M119" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration
dropped to a low value on the 5th December (Fig. 14a, d). Due to the absence
of cloud/fog blocking, more longwave radiation from the ground was emitted
into space on the night of 6th December, and the surface net radiant exposure decreased
from <inline-formula><mml:math id="M120" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.58</mml:mn></mml:mrow></mml:math></inline-formula> on the 5th to <inline-formula><mml:math id="M121" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1.45</mml:mn></mml:mrow></mml:math></inline-formula> on the 6th December (2.5 times; Fig. 14e).
The significant reduction in the surface radiation cooled the
near-surface atmospheric temperature, which formed an inversion layer of
approximately 50–100 m (Fig. 14c). Capped by the inversion
layer, the PM<inline-formula><mml:math id="M122" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration doubled after the night of
the 6th December to form another aerosol pollution event (Fig. 14a).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F14"><label>Figure 14</label><caption><p id="d1e1978">Temporal variations in PM<inline-formula><mml:math id="M123" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation and vertical
distributions of meteorological factors from 4 to 7 December 2017.
<bold>(a)</bold> PM<inline-formula><mml:math id="M124" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration, <bold>(b)</bold> winds
(vectors) and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and
<bold>(e)</bold> global radiant exposure.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f14.png"/>

        </fig>

      <p id="d1e2021">Pollution removal in Chengdu mainly relies on northeasterly winds to blow
pollution away. The winds also carry water vapor to add humidity to the
atmosphere above Chengdu, which converts pollutants into fog/cloud drops or
produces precipitation that removes pollutants through wet removal (Fig. 12d, blue boxes).</p>
</sec>
<sec id="Ch1.S3.SS6">
  <title>The two-way feedback mechanism on the windy Northeast China Plain</title>
      <p id="d1e2030">The Northeast China Plain lies north of the Liaodong Gulf, west of the
Changbai Mountains, east of the Greater Khingan Range, and south of the Lesser
Khingan Range (Fig. 2). Due to the low mountains to the northwest, the Northeast
China Plain is susceptible to cold, dry northerly air from Siberia in
winter. As the largest city in northeastern China with respect to its urban
population, Shenyang is located in the southwestern Northeast China Plain
(Fig. 2), where the warm, humid southwesterly flows are transported from Bohai Bay.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F15" specific-use="star"><label>Figure 15</label><caption><p id="d1e2035">Temporal variations in PM<inline-formula><mml:math id="M125" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>, surface radiation and vertical
distributions of meteorological factors from 1 December 2016 to 10 January 2017.
<bold>(a)</bold> PM<inline-formula><mml:math id="M126" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration, <bold>(b)</bold> winds
(vectors) and wind velocity, <bold>(c)</bold> temperature, <bold>(d)</bold> RH, and
<bold>(e)</bold> global radiant exposure. (Blue lines: HPEs; light blue lines: PEs;
white or red boxes: CSs; and blue dots: precipitation.)</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f15.png"/>

        </fig>

      <p id="d1e2078"><?xmltex \hack{\newpage}?>From 1 December 2016 to 10 January 2017, six aerosol pollution episodes
occurred in Shenyang (Fig. 15a, blue boxes), four of which persisted for
more than 3 days with peak PM<inline-formula><mml:math id="M127" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentrations greater than
200 <inline-formula><mml:math id="M128" 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="M129" 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> (Fig. 15a). During these HPEs, we observed surface
radiation reductions, near-surface inversions, low-level RH enhancement, and
increases in the PM<inline-formula><mml:math id="M130" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration (Fig. 15a–d, red and
white boxes) under slight or calm winds (Fig. 15b, red boxes); these
conditions indicate the occurrence of the two-way feedback mechanism in Shenyang.</p>
      <p id="d1e2120">To quantify the magnitude of the two-way feedback during TSs and CSs in
Shenyang, the air temperature difference between the radiosonde observations
at 20:00 BJT and the ERA-Interim reanalysis data was obtained. Similar to
Beijing and Xi'an, the temperature profile was also modified by aerosols
during both the TSs and the CSs (Fig. 16a, b). However, the magnitude of
near-ground cooling bias was lower than that in Beijing and Xi'an, which is
due to relatively light aerosol pollution and windy conditions. From TSs to
CSs, the negative temperature difference at 1000 hPa increased from
<inline-formula><mml:math id="M131" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.26</mml:mn></mml:mrow></mml:math></inline-formula> to <inline-formula><mml:math id="M132" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">0.93</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M133" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C. The mean aerosol-induced cooling bias
(1000 hPa) in the TSs was only 28.2 % of that in the CSs.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F16"><label>Figure 16</label><caption><p id="d1e2155">Vertical mean temperature difference between sounding observations and
ERA-Interim reanalysis data during TSs and CSs in Shenyang from 1 December 2016
to 10 January 2017.</p></caption>
          <?xmltex \igopts{width=142.26378pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f16.png"/>

        </fig>

      <?pagebreak page3300?><p id="d1e2164"><?xmltex \hack{\newpage}?>Compared with those in Xi'an, Nanjing, Wuhan, Qingyuan, and Chengdu, the
speeds of the southeasterly or northwesterly winds are strikingly higher in
Shenyang. Relatively strong mid-lower southeasterly winds originate from
Bohai Bay, and these winds transport warm, humid air that heats and adds
humidity to the mid-upper layer above Shenyang (Fig. 15c, d). This air
also transports aerosol pollutants to Shenyang because it carries pollutants
from populated and polluted southwestern industrial regions, including
Anshan. Lower strong northwesterly winds carry dry, cold air from Siberia to
remove pollutants in Shenyang (Fig. 15a, b).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F17" specific-use="star"><label>Figure 17</label><caption><p id="d1e2170">Vertical temperature difference between sounding observations and
ERA-Interim reanalysis data under different concentration bins of PM<inline-formula><mml:math id="M134" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
mass (<inline-formula><mml:math id="M135" 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="M136" 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 <bold>(a)</bold> Beijing, <bold>(b)</bold> Xi'an,
<bold>(c)</bold> Shenyang, <bold>(d)</bold> Wuhan, <bold>(e)</bold> Nanjing, and <bold>(f)</bold> Qingyuan.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f17.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS7">
  <title>Quantifying the two-way feedback mechanism and comparing its magnitude</title>
      <p id="d1e2233">As previously mentioned, the weak two-way feedback mechanism in the Sichuan
Basin is weakened by the cloudy mid-upper layers, which compete with the
near-surface aerosols for solar radiation. However, the mechanism occurred
in the Guanzhong Plain, the middle and lower reaches of the Yangtze River,
the Pearl River Delta region, and the Northeast China Plain. Dominant
scattering aerosols will scatter solar radiation to cause near-ground
temperature reduction; absorbing aerosols will absorb solar radiation to
heat the upper aerosol layer and subsequently cool the near-ground layer
(Boucher et al., 2013a). Previous studies have found that aerosol profiles
play an important role in the radiative cooling effects of aerosols, which
results in vertical differences in meteorological factors (Wilcox et al.,
2016; Wang et al., 2018). To quantify the magnitude of the two-way feedback
in these haze regions of China, we obtained the vertical air temperature
difference between the radiosonde observations affected by this two-way
feedback and the ERA-Interim reanalysis data without feedback in the
regional center cities, including Beijing, Xi'an, Shenyang, Wuhan, Nanjing,
and Qingyuan (as a substitute for Guangzhou). A previous study established a
threshold value for the PM<inline-formula><mml:math id="M137" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration (100 <inline-formula><mml:math id="M138" 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="M139" 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>)
that effectively activates the<?pagebreak page3301?> two-way feedback in HPEs; additionally,
a lower threshold value (71 <inline-formula><mml:math id="M140" 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="M141" 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>) has been identified for
lighter HPEs (Zhong et al., 2019). Therefore, based on the
diurnal mean PM<inline-formula><mml:math id="M142" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration (from 08:00 to 17:00 BJT), the
temperature difference is further classified by the criterion of
100 <inline-formula><mml:math id="M143" 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="M144" 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 the more polluted North China Plain, Guanzhong Plain, and
Northeast China Plain and by the criterion of 71 <inline-formula><mml:math id="M145" 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="M146" 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 the
less polluted Two Lakes Plain, Yangtze River Delta, and Pearl River Delta.</p>
      <p id="d1e2335">By comparing the air temperature difference below and above these thresholds
in the six cities, we found that the lower temperature profile was
strikingly modified by the two-way feedback mechanism (Fig. 17). On the
North China Plain, the Guanzhong Plain, and the Northeast China Plain, the
lower temperature bias between the sounding observations and the ERA-Interim
data was close to zero below the threshold of 100 <inline-formula><mml:math id="M147" 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="M148" 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> but
immediately became negative above the threshold (Fig. 17a–c). In the
Two Lakes Plain, the Yangtze River Delta, and the Pearl River Delta, we
observed a similar reduction in the temperature difference below and above
the threshold of 71 <inline-formula><mml:math id="M149" 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="M150" 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> (Fig. 17d–f). Overall, the
magnitude of the two-way feedback mechanism was larger in the North China
Plain, the Guanzhong Plain, and the Northeast China Plain than in the Two
Lakes Plain, the Yangtze River Delta, and the Pearl River Delta.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F18" specific-use="star"><label>Figure 18</label><caption><p id="d1e2380">Correlation between PLAM and PM<inline-formula><mml:math id="M151" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> during the typical rising
processes of PM<inline-formula><mml:math id="M152" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> from 1 December 2016 to 10 January 2017.</p></caption>
          <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f18.png"/>

        </fig>

      <p id="d1e2407">For each representative site, the low-level cooling bias was more striking
near the surface; additionally, as the PM<inline-formula><mml:math id="M153" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration
increased, the low-level cooling bias became more significant (Fig. 17). In
Beijing, the negative temperature difference reached more than 2 <inline-formula><mml:math id="M154" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C
with PM<inline-formula><mml:math id="M155" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> values in the range of 200–300 <inline-formula><mml:math id="M156" 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="M157" 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>
compared to approximately 1 <inline-formula><mml:math id="M158" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in the range of
100–200 <inline-formula><mml:math id="M159" 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="M160" 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 Xi'an, the temperature
difference decreased from approximately <inline-formula><mml:math id="M161" 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="M162" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in the range of
100–200 <inline-formula><mml:math id="M163" 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="M164" 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> to 2.5 <inline-formula><mml:math id="M165" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in the range of
200–300 <inline-formula><mml:math id="M166" 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="M167" 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 Shenyang, the cooling bias
of approximately 0.6 <inline-formula><mml:math id="M168" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C occurred with the increase in PM<inline-formula><mml:math id="M169" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> from
100–200 to 200–300 <inline-formula><mml:math id="M170" 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="M171" 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>. Under the most polluted conditions, the near-ground cooling
bias was greater than <inline-formula><mml:math id="M172" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M173" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, approximately <inline-formula><mml:math id="M174" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M175" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, and
approximately <inline-formula><mml:math id="M176" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M177" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in Beijing, Xi'an, and Shenyang, respectively,
which was substantially affected by the two-way feedback.</p>
      <p id="d1e2654">To quantify the feedback of deteriorating meteorological conditions on the
increasing PM<inline-formula><mml:math id="M178" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> in the CSs, the PLAM index was used, which mainly
reflects the stability of the air mass and the condensation rate of water
vapor on aerosol particles. The squared correlation coefficients between the
hourly PLAM and PM<inline-formula><mml:math id="M179" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentration in the typical processes of PM<inline-formula><mml:math id="M180" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
increase during the CSs were 0.71, 0.7, 0.72, 0.68, 0.64, and 0.63
in Beijing, Xi'an, Shenyang, Wuhan, Nanjing, and Qingyuan, respectively
(Fig. 18a–f); these values exceeded the 0.05 significance
level, which suggested that such a meteorological feedback on PM<inline-formula><mml:math id="M181" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
explained 60 %–70 % of the increase in the PM<inline-formula><mml:math id="M182" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> during the CSs.</p>
</sec>
</sec>
<sec id="Ch1.S4" sec-type="conclusions">
  <title>Conclusions</title>
      <p id="d1e2709">Here, we used PM<inline-formula><mml:math id="M183" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> observations, surface radiation data, radiosonde
observations, the PLAM index, and ERA-Interim reanalysis data to
investigate the formation, accumulation, and dispersion of aerosol pollution
during persistent heavy aerosol pollution episodes (HPEs) over 3 days,
specifically focusing on the two-way feedback mechanism between
unfavorable meteorological conditions and the cumulative PM<inline-formula><mml:math id="M184" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
pollution in various haze regions in China, including the Guanzhong Plain,
the Yangtze River Delta region, the Two Lakes Basin, the Pearl River Delta,
the Sichuan Basin, and the Northeast China Plain.</p>
      <?pagebreak page3302?><p id="d1e2730">On the Guanzhong Plain, we observed a striking two-way feedback mechanism,
including reduced surface radiation, near-surface inversions, RH
enhancement in the lower part of the BL, and increases in PM<inline-formula><mml:math id="M185" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass
concentrations under slight or calm winds in the CSs. For the representative
site of Xi'an, the near-ground cooling bias caused by the two-way feedback
was as high as approximately <inline-formula><mml:math id="M186" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M187" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, which was similar to
that observed in Beijing. Bordered by the Qinling Mountains and the Loess
Plateau, the Guanzhong Plain experiences inter-regional pollution transport
below the BL, e.g., pollution transport to Xi'an from Yuncheng and Linfen
under lower northwesterly winds in the TSs. Pollution clearing mainly
depends on the lower strong northeasterly winds to blow pollutants away and
the mid-upper southerly winds to transport water vapor to increase RH, which
causes the PM<inline-formula><mml:math id="M188" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> to enter the fog-cloud phase.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F19" specific-use="star"><label>Figure 19</label><caption><p id="d1e2772">A concept scheme of pollution removal <bold>(a)</bold>, transport <bold>(b)</bold>,
and accumulation <bold>(c)</bold>, specifically the two-way feedback mechanism
between unfavorable meteorological conditions and cumulative aerosol pollution <bold>(c)</bold>.</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://acp.copernicus.org/articles/19/3287/2019/acp-19-3287-2019-f19.png"/>

      </fig>

      <?pagebreak page3303?><p id="d1e2793">In the relatively less polluted Yangtze River Delta region, the aerosol
pollution formation is similar to that in Beijing, including earlier TSs and
later CSs. During the TSs, the Yangtze River Delta region is affected by
trans-regional pollution transport below and above the BL from the North
China Plain, which induces increases in the PM<inline-formula><mml:math id="M189" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> at the near-surface or
in the higher atmosphere in this region, including Nanjing and Shanghai.
Upper transported pollutants then move downward to increasingly worsen the near-ground
aerosol pollution. During the CSs, we also observed the two-way
feedback mechanism, but its magnitude was lower than that in Beijing due to
the less-polluted conditions. In this region, pollution clearing relies on
persistent stronger northerly winds to carry pollutants out of this area, or
strong southeasterly winds, which transport clean, warm, humid air that
blows pollutants away or increases ambient RH to cause the PM<inline-formula><mml:math id="M190" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> to
enter the liquid fog-cloud phase. Similar to the Yangtze River Delta region,
the Two Lakes Basin also experienced trans-regional pollution transport from
the North China Plain under northerly winds below and sometimes above the BL
during the TSs. During the CSs, the two-way feedback is activated, and the
aerosol pollution worsens. In addition to the blowing effect of strong,
persistent northerly winds, pollution clearing also depends on the mid-upper
southerly winds, particularly the southwesterly winds, to transport water
vapor, which enhances the RH and eliminates pollutants through fog–cloud
conversion and wet removal.</p>
      <p id="d1e2815">In the least polluted Pearl River Delta, no feedback mechanism was observed
with PM<inline-formula><mml:math id="M191" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass concentrations below the threshold. However, when the
PM<inline-formula><mml:math id="M192" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentration exceeded the threshold, the two-way feedback
occurred in the CSs. The delta region was purified by lower clean, cold
northeasterly winds from the northern mountains and humidified by upper
southerly winds from the South China Sea.</p>
      <p id="d1e2836">The Sichuan Basin is dominated by high RH and weak winds; thus, the two-way
feedback mechanism was weakened by thick mid-upper fog/clouds that compete
with the near-surface aerosols for solar radiation and consequently<?pagebreak page3304?> cool the
whole atmosphere below. With the weak two-way feedback, the PM<inline-formula><mml:math id="M193" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> mass
concentration increased under lower slight or calm winds and was capped by
the upper temperature inversions caused by the upper southwesterly winds
from the Tibetan Plateau. Pollution clearing mainly relies on northeasterly
winds to blow pollutants away, and these winds also add humid air to the
atmosphere, which converts aerosols into fog/cloud drops. Although
pollutants and water vapor are cleared, aerosol pollution soon forms
again due to more longwave radiation lost from the ground, which results in
rare effective pollution clearing in the Sichuan Basin.</p>
      <p id="d1e2848">Compared with the above regions, the southerly and northerly winds are
strikingly stronger in the Northeast China Plain. Strong mid-lower
southeasterly winds originate from Bohai Bay, transport warm, humid air that
heats and adds humidity to the inland area, and transport pollutants
inter-regionally from polluted southwestern industrial regions. Lower strong
northwesterly winds carry dry, cold air from Siberia to remove pollutants.
At the representative site in Shenyang, a two-way feedback mechanism also
exists during the CSs with slight or calm winds.</p>
      <p id="d1e2851">The transport, accumulation, and removal of pollution described above are
visually illustrated in a conceptual scheme (Fig. 19), which particularly
highlights the effect of the two-way feedback mechanism regarding its role in
intensifying the HPEs. Due to the occurrence of a two-way feedback
mechanism, effective pollution control could further mitigate aerosol
pollution, whereas persistent worsening aerosol pollution could lead to an
additional increase in PM<inline-formula><mml:math id="M194" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>. Given the inter-regional and
trans-regional pollution transport, the control of regional emissions among
key haze regions in China, to reduce the transport of pollutants or permit them from reaching
the threshold which triggers the two-way feedback mechanism, is
essential to substantially reduce persistent heavy aerosol pollution episodes.
At the same time, these results also show that, even in
favorable weather conditions, aerosol pollutant emissions should not be
allowed to occur without restrictions; when aerosol pollution cumulates to a
certain extent, it will significantly worsen the BL meteorological
conditions and “close” the “meteorological channels” available for pollution dispersion.</p>
</sec>

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

      <p id="d1e2868">The data that support the findings of this study are available
from the corresponding author upon reasonable request.</p>
  </notes><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e2874">XZ and YW designed the research, and XZ and JZ carried out
the analysis of observations. TW provided UAV observations. JW provided PLAM
data. XS conducted a supplementary analysis. JZ wrote the first manuscript
and XZ revised it. All authors contributed to the improvement of this manuscript
and approved the final version of the paper.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e2880">The authors declare that they have no conflict of interest.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e2886">This research was supported by the National Key Project of MOST (grant no. 2016YFC0203306)
and the Atmospheric Pollution Control of the Prime Minister Fund (grant no. DQGG0104). <?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: Qiang Zhang <?xmltex \hack{\newline}?>
Reviewed by: two anonymous referees</p></ack><?xmltex \hack{\newpage}?><?xmltex \hack{\newpage}?><ref-list>
    <title>References</title>

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    <!--<article-title-html>The two-way feedback mechanism between unfavorable  meteorological conditions and cumulative aerosol  pollution in various haze regions of China</article-title-html>
<abstract-html><p>Accompanied by unfavorable meteorological conditions with stable
stratification in various haze regions of China, persistent heavy aerosol
pollution episodes (HPEs) lasting more than 3 consecutive days frequently
occur, particularly in winter. In the North China Plain (NCP), explosive
growth of fine particulate matter smaller than 2.5&thinsp;µm in
diameter (PM<sub>2.5</sub>), which occurs during some HPE<sub>S</sub>, is dominated by
a two-way feedback mechanism between more unfavorable
meteorological conditions and cumulative aerosol pollution. However, the
existence of a two-way feedback mechanism such as this in other key haze regions in China is
uncertain; these regions include the Guanzhong Plain (GZP), the Yangtze River
Delta (YRD) region, the Two Lakes Basin (TLB; a large outflow basin connected to Hubei Province and Hunan Province), the Pearl River Delta (PRD)
region, the Sichuan Basin (SB), and the Northeast China Plain (NeCP). In this
study, using surface PM<sub>2.5</sub> and radiation observations, radiosonde
observations, and reanalysis data, we observed the existence of a two-way
feedback mechanism in the six abovementioned regions. In the SB, this two-way feedback
mechanism is weak due to the suppression of cloudy mid-upper layers. In the
more polluted NCP, the GZP, and the NeCP, the feedback is more striking than
that in the YRD, the TLB, and the PRD. In these regions, the feedback of
worsened meteorological conditions on PM<sub>2.5</sub> explains 60&thinsp;%–70&thinsp;% of
the increase in PM<sub>2.5</sub> during the cumulative stages (CSs). For each
region, the low-level cooling bias becomes increasingly substantial with
increasing aerosol pollution and a closer distance to the ground surface.
With PM<sub>2.5</sub> mass concentrations greater than 400&thinsp;µg&thinsp;m<sup>−3</sup>,
the near-ground bias exceeded −4&thinsp;°C in Beijing and reached up to
approximately −4&thinsp;°C in Xi'an; this result was caused by
accumulated aerosol mass to some extent. In addition to the increase in
PM<sub>2.5</sub> caused by the two-way feedback, these regions also suffer from the
regional transport of pollutants, including inter-regional transport in the
GZP, trans-regional transport from the NCP to the YRD and the TLB, and
southwesterly transport in the NeCP.</p></abstract-html>
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