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<journal-id journal-id-type="publisher-id">Front. Environ. Sci.</journal-id>
<journal-title>Frontiers in Environmental Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Environ. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-665X</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="publisher-id">1267627</article-id>
<article-id pub-id-type="doi">10.3389/fenvs.2024.1267627</article-id>
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<subj-group subj-group-type="heading">
<subject>Environmental Science</subject>
<subj-group>
<subject>Original Research</subject>
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<title-group>
<article-title>The variability of NO<sub>2</sub> concentrations over China based on satellite and influencing factors analysis during 2019&#x2013;2021</article-title>
<alt-title alt-title-type="left-running-head">Zhang et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fenvs.2024.1267627">10.3389/fenvs.2024.1267627</ext-link>
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<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Yuhuan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/software/"/>
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<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Linhan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Guo</surname>
<given-names>Wei</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1815868/overview"/>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhou</surname>
<given-names>Chunyan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Zhengqiang</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
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<uri xlink:href="https://loop.frontiersin.org/people/1038331/overview"/>
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<aff id="aff1">
<sup>1</sup>
<institution>Satellite Application Center for Ecology and Environment</institution>, <institution>Ministry of Ecology and Environment of the People&#x2019;s Republic of China</institution>, <addr-line>Beijing</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>College of Geoscience and Surveying Engineering</institution>, <institution>China University of Mining and Technology</institution>, <addr-line>Beijing</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Aerospace Information Research Institute</institution>, <institution>Chinese Academy of Sciences</institution>, <addr-line>Beijing</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1128436/overview">Alireza Sharifi</ext-link>, Shahid Rajaee Teacher Training University, Iran</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1261831/overview">Luis Gerardo Ruiz Su&#xe1;rez</ext-link>, National Autonomous University of Mexico, Mexico</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2550944/overview">Hadi Mahdipour</ext-link>, University of Oviedo, Spain</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1267735/overview">Lipika Deka</ext-link>, De Montfort University, United Kingdom</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Wei Guo, <email>weiguo@cumtb.edu.cn</email>, Chunyan Zhou, <email>zhoucy@secmep.cn</email>
</corresp>
</author-notes>
<pub-date pub-type="epub">
<day>22</day>
<month>02</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>12</volume>
<elocation-id>1267627</elocation-id>
<history>
<date date-type="received">
<day>26</day>
<month>07</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>02</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Zhang, Chen, Guo, Zhou and Li.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Zhang, Chen, Guo, Zhou and Li</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>The variation of tropospheric nitrogen dioxide (NO<sub>2</sub>) vertical column densities (VCDs) indirectly reflects the difference in pollution emissions from industrial production and transportation. Accurately analyzing its pollution sources and driving factors plays an important role in energy conservation, emission reduction, and air pollution reduction. NO<sub>2</sub> concentration products of Sentinel-5P (Sentinel-5 Precursor) TROPOMI (TROPOspheric Monitoring Instrument) from 2019 to 2021 and Aura OMI (Ozone Monitoring Instrument) from 2009 to 2021, combined with China&#x2019;s main energy consumption, the growth value of the industry, Gross Domestic Product (GDP), and other data were used to analyze the influencing factors of NO<sub>2</sub> variations. Firstly, NO<sub>2</sub> tropospheric vertical column densities (NO<sub>2</sub> TVCDs) of China increased by 14.72% and 3.26% in 2021 and 2020 compared with the 2019. The secondary and tertiary industry and the national energy consumption increased synchronously, which was highly related to the increase in NO<sub>2</sub> TVCDs. Secondly, the impact of COVID-19 (coronavirus disease 2019) on China&#x2019;s industrial production and residents was mainly concentrated in the first quarter of 2020, which leading to a decline in the annual average NO<sub>2</sub> concentration in densely populated areas in 2020 compared to the same period in 2019. The industrial production scale and production capacity has gradually recovered since April 2020, and the NO<sub>2</sub> concentration has gradually reached or exceeded the level of the same period of 2019. Finally, atmospheric pollution prevention and control measures played a positive role in the decline of NO<sub>2</sub> of China.</p>
</abstract>
<kwd-group>
<kwd>tropospheric NO<sub>2</sub> column density</kwd>
<kwd>COVID-19</kwd>
<kwd>TROPOMI</kwd>
<kwd>OMI</kwd>
<kwd>industry value added</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Atmosphere and Climate</meta-value>
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</front>
<body>
<sec id="s1">
<title>1 Introduction</title>
<p>NO<sub>2</sub> is a trace gas in the atmosphere, it playing an important role in tropospheric and stratospheric chemical reactions (<xref ref-type="bibr" rid="B35">Velders et al., 2001</xref>). And it is also a primary pollutant that forms acid rain, acid fog, and other air pollution phenomena (<xref ref-type="bibr" rid="B46">Zhou et al., 2016a</xref>). NO<sub>2</sub> have much to do with the formation and extinction of ozone in atmosphere and is also an essential precursor of PM<sub>2.5</sub> (Particulate Matter) (<xref ref-type="bibr" rid="B22">Qin and Zhao, 2003</xref>; <xref ref-type="bibr" rid="B47">Zhou et al., 2016b</xref>). NO<sub>2</sub> has both natural and anthropogenic sources (<xref ref-type="bibr" rid="B29">Tang et al., 2005</xref>). Anthropogenic sources include the combustion of fossil fuels, such as in transportation, the petrochemical industry, and coal power plants (<xref ref-type="bibr" rid="B12">Lee et al., 1997</xref>; <xref ref-type="bibr" rid="B3">Bradshaw et al., 2000</xref>), which account for about 2/3 of the total emissions (<xref ref-type="bibr" rid="B28">Solomon et al., 2007</xref>). Other sources include soil emissions and lightning generation (<xref ref-type="bibr" rid="B14">Lin, 2011</xref>). NO<sub>2</sub> is one of the indicators of the air pollution level, and can reflect anthropogenic activities to some extent (<xref ref-type="bibr" rid="B30">Tao et al., 2020</xref>). Therefore, it is necessary to monitor and analyze the changes of NO<sub>2</sub> in the atmosphere accurately.</p>
<p>Atmospheric NO<sub>2</sub> concentration monitoring data sources can be categorized into ground-based monitoring, airborne observation, and satellite observation. Satellite remote sensing monitoring can quickly obtain information at a large spatial scale, and is increasingly used for atmospheric monitoring (<xref ref-type="bibr" rid="B10">Fishman et al., 2008</xref>; <xref ref-type="bibr" rid="B19">Martin, 2008</xref>). The observation of NO<sub>2</sub> TVCDs by satellite began in the mid-1990s. The satellite ERS-2 (European Remote Sensing ERS) launched by the ESA (European Space Agency) on 21 April 1995, had the instrument of GOME (Global Ozone Monitoring Experiment), it can monitor the global distribution of some trace gases, such as NO<sub>2</sub>, SO<sub>2</sub> (<ext-link ext-link-type="uri" xlink:href="https://en.wikipedia.org/wiki/Sulfur_dioxide">sulfur dioxide</ext-link>), and HCHO (formaldehyde). The instrument of GOME enabled the monitoring of the distribution of NO<sub>2</sub> on a global scale for the first time (<xref ref-type="bibr" rid="B20">Martin et al., 2002</xref>; <xref ref-type="bibr" rid="B23">Richter and Burrows, 2002</xref>; <xref ref-type="bibr" rid="B43">Zhang et al., 2012</xref>). The ENVISAT-1 (Environmental Satellite-1) launched by ESA on 1 March 2002, had the instrument of SCIAMACHY (The Scanning Imaging Absorption Spectrometer for Atmospheric Cartography), it was used to monitor trace gases such as NO<sub>2</sub> in the troposphere and stratosphere (<xref ref-type="bibr" rid="B41">Yao et al., 2012</xref>). OMI (Ozone Monitoring Instrument) carried on the EOS (Earth Observing System) Aura satellite launched by NASA (National Aeronautics and Space Administration) on 15 July 2004, it can obtain daily monitoring results for NO<sub>2</sub> in the global atmospheric troposphere (<xref ref-type="bibr" rid="B2">Boersma et al., 2007</xref>). Aura-OMI is widely used due to its high spatial resolution (13 &#xd7; 24&#xa0;km<sup>2</sup> in sub-satellite pixels) and stable on-orbit time (<xref ref-type="bibr" rid="B16">Liu et al., 2015</xref>; <xref ref-type="bibr" rid="B48">Zhou et al., 2016c</xref>). S5P (the Sentinel 5 precursor) with a sun-synchronous orbit satellite was launched on 13 October 2017 (<xref ref-type="bibr" rid="B34">Veefkind et al., 2012</xref>), it is designed for monitoring global air quality and acquiring the atmospheric composition information daily, includingNO<sub>2</sub>, SO<sub>2</sub>, O<sub>3</sub>, CO (carbon monoxide), CH<sub>4</sub>, HCHO (formaldehyde), and aerosol (<xref ref-type="bibr" rid="B34">Veefkind et al., 2012</xref>; <xref ref-type="bibr" rid="B39">Wang and Su, 2020</xref>). The instrument of TROPOMI was loaded on S5P, it has eight spectral bands covering from UV (ultraviolet) to SWIR (shortwave infrared) wavelengths. TROPOMI data can be used to analyze the distribution characteristics quantitatively and the trends of NO<sub>2</sub> in China to evaluate the impact of anthropogenic pollution on the environment and climate. TROPOMI&#x2019;s higher spatial resolution (7 &#xd7; 3.5&#xa0;km<sup>2</sup>) can identify small pollution sources and their accurate location (<xref ref-type="bibr" rid="B9">Fioletov et al., 2013</xref>; <xref ref-type="bibr" rid="B15">Liu et al., 2020</xref>).</p>
<p>China is a large developing country. In the past few decades, the rapid economic development has been accompanied by great changes in the atmosphere situation (<xref ref-type="bibr" rid="B7">Fan et al., 2020</xref>). The Chinese government attaches great importance to environmental problems and has taken a series of measures to reduce air pollution (<xref ref-type="bibr" rid="B24">Ronald et al., 2017</xref>; <xref ref-type="bibr" rid="B27">Sogacheva et al., 2018</xref>). In addition to this, the COVID-19 (Coronavirus Disease 2019) broke out in late 2019 and spread widely in 2020, a number of measures have been taken to reduce the spread of the virus, such as social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires (&#x201c;lockdowns&#x201d;), these measures affect industrial production and transportation, which in turn affect the emission of air pollutants (<xref ref-type="bibr" rid="B26">Silver et al., 2020</xref>). The effect of these measures can be seen through satellite monitoring of air pollutants (<xref ref-type="bibr" rid="B7">Fan et al., 2020</xref>; <xref ref-type="bibr" rid="B30">Tao et al., 2020</xref>).</p>
<p>The single NO<sub>2</sub> space-time variation analysis was generally conducted (<xref ref-type="bibr" rid="B3">Bradshaw et al., 2000</xref>; <xref ref-type="bibr" rid="B37">Wang et al., 2020</xref>), or add some simple influencing factors, such as number of motor vehicles (<xref ref-type="bibr" rid="B46">Zhou et al., 2016a</xref>; <xref ref-type="bibr" rid="B8">Fan et al., 2021</xref>) generally. Some researchers also analyze changes of atmospheric pollutants, including NO<sub>2</sub>, over a particular period of time (<xref ref-type="bibr" rid="B7">Fan et al., 2020</xref>; <xref ref-type="bibr" rid="B15">Liu et al., 2020</xref>; <xref ref-type="bibr" rid="B30">Tao et al., 2020</xref>). Few people have combined NO<sub>2</sub> with actual GDP and energy consumption, and environmental management policy for comprehensive analysis and evaluation, however these are all related to NO<sub>2</sub> emissions.</p>
<p>In this paper, OMI provided long time series data for annual trend monitoring over 10 years, while TROPOMI has a higher spatial resolution and was used for regional statistical analysis and monthly trend monitoring from 2019 to 2021. This paper monitored the distribution and variation of NO<sub>2</sub> TVCDs from 2009 to 2021 in China using NO<sub>2</sub> production of TROPOMI and OMI, then analyzed the causes of variation combined with China&#x2019;s main energy consumption, Gross Domestic Product (GDP), industrial growth value, and other data. The NO<sub>2</sub> TVCDs of 2019 was used as the baseline value, and the reason for NO<sub>2</sub> variation was comprehensively analyzed based on the values from 2020 to 2021. The measures related to COVID-19 also had impact on NO<sub>2</sub> emissions (<xref ref-type="bibr" rid="B36">V&#xee;rghileanu et al., 2020</xref>; <xref ref-type="bibr" rid="B37">Wang et al., 2020</xref>; <xref ref-type="bibr" rid="B1">Ali et al., 2021</xref>; <xref ref-type="bibr" rid="B49">Zhou et al., 2021</xref>), so we also analyzed the impact of COVID-19 in this paper.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Study area</title>
<p>The study area is China as <xref ref-type="fig" rid="F1">Figure 1</xref> shows with the NO<sub>2</sub> TVCDs of 2019. The NO<sub>2</sub> TVCDs of Central and eastern China was higher such as &#x201c;2 &#x2b; 26&#x201d;cities in Beijing-Tianjin-Hebei and surrounding areas, Yangtze River Delta region, Fen-Wei Plains, Pearl River Delta Region, Chengdu-Chongqing Region. Most of China&#x2019;s megacities and much industry are situated in these areas, resulting in a high density of motorized traffic. These intensive human activities result in emissions, leading to the deterioration of air quality (<xref ref-type="bibr" rid="B7">Fan et al., 2020</xref>). Air pollution in China is high in the east and low in the west as described in detail by Zheng (<xref ref-type="bibr" rid="B45">Zheng et al., 2019</xref>) for NO<sub>2</sub>, and 94% of the Chinese population lives in east China. We also separately analyzed the NO<sub>2</sub> TVCDs in these five typical regions. The details of these five typical regions are show in <xref ref-type="app" rid="app1">Appendix A</xref>.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>The study area with NO<sub>2</sub> TVCDs of 2019. The red polygons show the five typical areas which are used in this study (1: Beijing-Tianjin-Hebei and its surrounding &#x201c;2 &#x2b; 26&#x201d;cities, 2: Fen-Wei plains, 3: Yangtze River Delta, 4: Pearl River Delta, 5: Cheng-Yu district).</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g001.tif"/>
</fig>
</sec>
<sec id="s2-2">
<title>2.2 Materials</title>
<p>The NO<sub>2</sub> data used in this paper were monitored by both TROPOMI sensors loaded on S5P and OMI sensors loaded on Aura. The OMI NO<sub>2</sub> data were used widely before the launch of TROPOMI, and had obtained more than 15&#xa0;years of data, it can be used to study the global NO<sub>2</sub> distribution (<xref ref-type="bibr" rid="B16">Liu et al., 2015</xref>; <xref ref-type="bibr" rid="B48">Zhou et al., 2016c</xref>; <xref ref-type="bibr" rid="B37">Wang et al., 2020</xref>). These data are available from the NASA website (<ext-link ext-link-type="uri" xlink:href="https://giovanni.gsfc.nasa.gov/giovanni/">https://giovanni.gsfc.nasa.gov/giovanni/</ext-link>) (<xref ref-type="bibr" rid="B40">Wang et al., 2014</xref>). OMI level 3 products were used in this paper. The products contain annual mean NO<sub>2</sub> TVCDs with a spatial resolution of 0.25&#xb0; &#xd7; 0.25&#xb0;, these data were used for annual change analysis of NO<sub>2</sub> TVCDs in this paper.</p>
<p>As a new generation of atmospheric composition monitor, TROPOMI inherited the advantages of GOME, SCIAMACHY, OMI, and Ozone Mapping and Profiler Suite (OMPS), but has a higher spatial resolution and a more comprehensive wavelength range (<xref ref-type="bibr" rid="B44">Zhang et al., 2020</xref>). TROPOMI monitors vital atmospheric components (<xref ref-type="bibr" rid="B11">Kleipool et al., 2018</xref>), which can be used to quantitatively analyze the distribution, characteristics and trends of NO<sub>2</sub> in China. TROPOMI products used in this study are L3 offline (OFFL) version products. These products contain daily NO<sub>2</sub> TVCDs with a spatial resolution of 7&#xa0;km &#xd7; 3.5&#xa0;km, which were used in this paper. These data are available on the ESA website (<ext-link ext-link-type="uri" xlink:href="https://s5phub.copernicus.eu/">https://s5phub.copernicus.eu/</ext-link>) or NASA&#x2019;s website (<ext-link ext-link-type="uri" xlink:href="https://search.earthdata.nasa.gov/search">https://search.earthdata.nasa.gov/search</ext-link>) (<xref ref-type="bibr" rid="B33">van Geffen et al., 2020</xref>; <xref ref-type="bibr" rid="B44">Zhang et al., 2020</xref>).</p>
<p>OMI products were used to detect annual changes of NO<sub>2</sub> from 2009 to 2021, while TROPOMI products from 2019 to 2021 mainly used for NO<sub>2</sub> regional statistical analysis. The NO<sub>2</sub> TVCDs from TROPOMI has the higher spatial resolution, so the data were mainly used to analyze monthly averaged spatial variations and calculate time series over certain regions. OMI and TROPOMI products thus provided complementary information for different time periods and were used for different uses.</p>
<p>Total energy consumption, Coal consumption, Crude oil consumption, primary industry value added, secondary industry value added, tertiary industry value added, and GDP data of China comes from the National Bureau of Statistics of China. These data are publicly available on the National Bureau of Statistics website (<ext-link ext-link-type="uri" xlink:href="https://www.stats.gov.cn/">https://www.stats.gov.cn/</ext-link>).</p>
</sec>
<sec id="s2-3">
<title>2.3 Methods</title>
<p>The mainstream method using satellite remote sensing to monitor the NO<sub>2</sub> TVCD is the DOAS (Differential Optical Absorption Spectroscopy) algorithm (<xref ref-type="bibr" rid="B21">Platt et al., 1979</xref>; <xref ref-type="bibr" rid="B4">Burrows et al., 1999</xref>; <xref ref-type="bibr" rid="B42">Zara et al., 2018</xref>). The NO<sub>2</sub> data product was developed based on the DOAS retrieval method in the UV spectral range. NO<sub>2</sub> has strong absorption characteristics in the UV bands (<xref ref-type="bibr" rid="B4">Burrows et al., 1999</xref>; <xref ref-type="bibr" rid="B44">Zhang et al., 2020</xref>). Retrieval consisted of a three-step procedure: firstly, it removes the surface reflection and the scattering effect of aerosols, fills the ring effect caused by Raman scattering from atmospheric molecules, and removes the absorption effect of all other gases in these bands (<xref ref-type="bibr" rid="B4">Burrows et al., 1999</xref>; <xref ref-type="bibr" rid="B42">Zara et al., 2018</xref>; <xref ref-type="bibr" rid="B44">Zhang et al., 2020</xref>). The retrieval of a total NO<sub>2</sub> slant column density from satellite data (OMI or TROPOMI) was achieved using a DOAS method. Secondly, the AMF (Air Mass Factor) was calculated based on radiative transfer model, and the NO<sub>2</sub> vertical column density was calculated (<xref ref-type="bibr" rid="B11">Kleipool et al., 2018</xref>). Finally, the stratospheric NO<sub>2</sub> column density was obtained through the atmospheric model or other methods, and the tropospheric NO<sub>2</sub> column density was obtained by removing it from the whole column density (<xref ref-type="bibr" rid="B31">Tao et al., 2009</xref>; <xref ref-type="bibr" rid="B17">Lorente et al., 2017</xref>)-.</p>
<p>China&#x2019;s daily NO<sub>2</sub> products were downloaded from the NASA and ESA website in the Network Common Data Form (NC) format. The tropospheric NO<sub>2</sub> concentration results were extracted, then synthetic product of monthly and annual were produced from daily results. The monthly monitoring results were obtained based by averaging daily NO<sub>2</sub> monitoring results (mean effective value), the annual monitoring results were obtained by averaging the monthly monitoring results (mean effective value) (<xref ref-type="table" rid="T1">Table 1</xref>). Region NO<sub>2</sub> is calculated by averaging effective pixels within the region.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Data acquisition methods.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Data</th>
<th align="center">Source</th>
<th align="center">Methods</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Annual China NO<sub>2</sub> TVCDs of TROPOMI</td>
<td align="center">Monthly China NO<sub>2</sub> TVCDs of TROPOMI</td>
<td align="center">Mean effective value</td>
</tr>
<tr>
<td align="center">Monthly China NO<sub>2</sub> TVCDs of TROPOMI</td>
<td align="center">Daily China NO<sub>2</sub> TVCDs of TROPOMI</td>
<td align="center">Mean effective value</td>
</tr>
<tr>
<td align="center">Annual China NO<sub>2</sub> TVCDs of OMI</td>
<td align="center">Annual NO<sub>2</sub> TVCDs products of OMI</td>
<td align="center">--</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Overall distribution of NO<sub>2</sub> TVCD from TROPOMI during 2019&#x2013;2021</title>
<sec id="s3-1-1">
<title>3.1.1 Annual variation of NO<sub>2</sub> TVCD in China</title>
<p>The NO<sub>2</sub> TVCD of China in 2019, 2020, and 2021 are shown in <xref ref-type="fig" rid="F2">Figure 2</xref>. The relative differences between 2021 and 2020, and 2021 and 2019, are shown in <xref ref-type="fig" rid="F3">Figure 3</xref>. The annual mean value, variation, and change rate of NO<sub>2</sub> TVCD in China and five typical regions from 2019 to 2021 are shown in <xref ref-type="table" rid="T2">Table 2</xref>.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>The annual average of NO<sub>2</sub> TVCD of China. <bold>(A)</bold>: 2019; <bold>(B)</bold>: 2020; <bold>(C)</bold>: 2021.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g002.tif"/>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Variation of NO<sub>2</sub> TVCD. <bold>(A)</bold>: 2021 compared to 2020; <bold>(B)</bold>: 2021 compared to 2019.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g003.tif"/>
</fig>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Annual mean value, variation, and change rate of NO<sub>2</sub> TVCD in China and five typical regions from 2019 to 2021.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Regions</th>
<th rowspan="2" align="center">Pixel number (TROPOMI)</th>
<th colspan="3" align="center">Mean of NO<sub>2</sub> TVCD (unit: 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup>)</th>
<th colspan="2" align="center">2021 compared with 2020</th>
<th colspan="2" align="center">2021 compared with 2019</th>
<th colspan="2" align="center">2020 compared with 2019</th>
</tr>
<tr>
<th align="center">2021</th>
<th align="center">2020</th>
<th align="center">2019</th>
<th align="center">Variation</th>
<th align="center">Change rate (%)</th>
<th align="center">Variation</th>
<th align="center">Change rate (%)</th>
<th align="center">Variation</th>
<th align="center">Change rate (%)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">China</td>
<td align="center">196527</td>
<td align="center">216.58</td>
<td align="center">188.79</td>
<td align="center">182.83</td>
<td align="center">27.79</td>
<td align="center">14.72</td>
<td align="center">33.75</td>
<td align="center">18.46</td>
<td align="center">5.96</td>
<td align="center">3.26</td>
</tr>
<tr>
<td align="center">&#x201c;2 &#x2b; 26&#x201d;cities in Beijing Tianjin Hebei and surrounding areas</td>
<td align="center">5618</td>
<td align="center">919.54</td>
<td align="center">804.96</td>
<td align="center">826.84</td>
<td align="center">114.58</td>
<td align="center">14.23</td>
<td align="center">92.70</td>
<td align="center">11.21</td>
<td align="center">&#x2212;21.88</td>
<td align="center">&#x2212;2.65</td>
</tr>
<tr>
<td align="center">Yangtze River Delta region</td>
<td align="center">6863</td>
<td align="center">618.15</td>
<td align="center">510.13</td>
<td align="center">501.65</td>
<td align="center">108.02</td>
<td align="center">21.18</td>
<td align="center">116.50</td>
<td align="center">23.22</td>
<td align="center">8.48</td>
<td align="center">1.69</td>
</tr>
<tr>
<td align="center">Fen-Wei Plains</td>
<td align="center">3095</td>
<td align="center">483.96</td>
<td align="center">443.84</td>
<td align="center">468.96</td>
<td align="center">40.12</td>
<td align="center">9.04</td>
<td align="center">15.00</td>
<td align="center">3.20</td>
<td align="center">&#x2212;25.12</td>
<td align="center">&#x2212;5.36</td>
</tr>
<tr>
<td align="center">Pearl River Delta Region</td>
<td align="center">964</td>
<td align="center">519.30</td>
<td align="center">439.74</td>
<td align="center">455.73</td>
<td align="center">79.56</td>
<td align="center">18.09</td>
<td align="center">63.57</td>
<td align="center">13.95</td>
<td align="center">&#x2212;15.99</td>
<td align="center">&#x2212;3.51</td>
</tr>
<tr>
<td align="center">Chengdu Chongqing Region</td>
<td align="center">4572</td>
<td align="center">398.52</td>
<td align="center">349.48</td>
<td align="center">311.00</td>
<td align="center">48.04</td>
<td align="center">14.04</td>
<td align="center">87.53</td>
<td align="center">28.14</td>
<td align="center">38.48</td>
<td align="center">12.37</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>It can be concluded that from 2019 to 2021 (<xref ref-type="table" rid="T2">Table 2</xref>), the country average NO<sub>2</sub> TVCD was 182.83, 188.79, and 216.58 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup>, respectively. NO<sub>2</sub> has been increasing year after year, with a growth rate of 3.26% and 14.72% in 2020 and 2021 compared to the previous year (<xref ref-type="fig" rid="F3">Figure 3</xref>; <xref ref-type="table" rid="T2">Table 2</xref>). The spatial distribution of NO<sub>2</sub> was consistent over the 3&#xa0;years (<xref ref-type="fig" rid="F2">Figure 2</xref>): the densely populated areas such as central and eastern regions, the Pearl River Delta region, Cheng-Yu district, and some regions of Xinjiang, has a higher NO<sub>2</sub> TVCD, while sparsely populated areas such as the western and northern regions, has a lower NO<sub>2</sub> TVCD.</p>
<p>
<xref ref-type="table" rid="T2">Table 2</xref> shows that in 2021, the average NO<sub>2</sub> TVCDs of China increased by 14.72% compared with 2020 and by 18.46% compared with 2019. Tropospheric NO<sub>2</sub> in some areas of Shanxi, Shaanxi, Inner Mongolia, Beijing, Tianjin, Hebei, and other provinces (autonomous regions and cities) was decreased in 2021 compared with 2019, while most of the other regions had an upward trend.</p>
<p>The differences of NO<sub>2</sub> TVCD between 2020 and 2019 are shown in <xref ref-type="fig" rid="F4">Figure 4</xref>. <xref ref-type="table" rid="T2">Table 2</xref> and <xref ref-type="fig" rid="F4">Figure 4</xref> show that the national wide NO<sub>2</sub> TVCD increased 3.26% in 2020 compared with 2019. The tropospheric NO<sub>2</sub> of Beijing, Tianjin, Hebei, Shanxi, Hubei, Shanghai, and some other regions decreased, while in Jilin, Heilongjiang, Sichuan, Guizhou, Yunnan, and other regions it increased.</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Variation of NO<sub>2</sub> TVCD in 2020 compared to 2019.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g004.tif"/>
</fig>
</sec>
<sec id="s3-1-2">
<title>3.1.2 Annual variation of NO<sub>2</sub> TVCD in typical areas</title>
<p>The statistics of the annual average value, variation, and change rate of NO<sub>2</sub> TVCD in five regions from 2009 to 2021 are shown in <xref ref-type="fig" rid="F5">Figure 5</xref> and <xref ref-type="table" rid="T2">Table 2</xref>. In 2021, the mean of NO<sub>2</sub> TVCD in Beijing-Tianjin-Hebei Region and surrounding &#x201c;2 &#x2b; 26&#x2033;cities were the highest, at up to 919.54 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup>, with an increase of 11.21% and 14.23% compared to 2019 and 2020, respectively. In 2021, the average NO<sub>2</sub> TVCD in the Yangtze River Delta was 618.15 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup>, with an increase of 23.22% and 21.18% compared to 2019 and 2020, respectively. In 2021, the mean NO<sub>2</sub> TVCDs in Fen-Wei plains, the Pearl River Delta, and the Chengdu-Chongqing Region were 483.96, 519.30, and 398.52 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup>, increased by 3.2%, 13.95%, and 28.14% compared with 2019, respectively.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Annual mean NO<sub>2</sub> TVCD in China and five typical regions from 2019 to 2021.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g005.tif"/>
</fig>
</sec>
</sec>
<sec id="s3-2">
<title>3.2 Monthly change of NO<sub>2</sub> TVCD from TROPOMI during 2019&#x2013;2021</title>
<p>The monthly average value, variation, and year-on-year change rate of NO<sub>2</sub> TVCDs in China from 2019 to 2021 were statistically analyzed, as shown in <xref ref-type="fig" rid="F6">Figure 6</xref> and <xref ref-type="table" rid="T3">Table 3</xref>. In 2021, China&#x2019;s highest monthly mean of NO<sub>2</sub> TVCD was 344.61 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup> in January, while the lowest value was 139.77 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup> in August. Compared with the same period in 2019, the monthly mean value of 2021 increased, with the largest increase rate of 35.03% in January. December took second place with an increase rate of 24%, and the smallest increase rate was 4.13% in September. Compared with the same period in 2020, the monthly mean value of 2021 increased except for December. The largest increase rate of 68.83% appeared in January, February took second place with an increase rate of 53.57%, while a decrease rate of 10% was observed in December. In 2020, China&#x2019;s highest monthly mean of NO<sub>2</sub> TVCD was 370.11 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup> in December, and the lowest value was 126.89 &#xd7; 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup> in February. A decrease rate of 20% was observed in January and February compared with the same period in 2019.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Monthly variations of NO<sub>2</sub> TVCD from 2019 to 2021.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g006.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>NO<sub>2</sub> TVCDs and year-on-year change rate.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Month</th>
<th colspan="3" align="center">Mean of NO<sub>2</sub> TVCDs (unit: 10<sup>13</sup>&#xa0;mol/cm<sup>2</sup>)</th>
<th colspan="2" align="center">Comparison between 2021 and 2020</th>
<th colspan="2" align="center">Comparison between 2021 and 2019</th>
<th colspan="2" align="center">Comparison between 2020 and 2019</th>
</tr>
<tr>
<th align="center">2021</th>
<th align="center">2020</th>
<th align="center">2019</th>
<th align="center">Variation</th>
<th align="center">Rate (%)</th>
<th align="center">Variation</th>
<th align="center">Rate (%)</th>
<th align="center">Variation</th>
<th align="center">Rate (%)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">January</td>
<td align="center">344.61</td>
<td align="center">204.12</td>
<td align="center">255.22</td>
<td align="center">140.49</td>
<td align="center">68.83</td>
<td align="center">89.39</td>
<td align="center">35.03</td>
<td align="center">&#x2212;51.10</td>
<td align="center">&#x2212;20.02</td>
</tr>
<tr>
<td align="center">February</td>
<td align="center">194.86</td>
<td align="center">126.89</td>
<td align="center">158.87</td>
<td align="center">67.97</td>
<td align="center">53.56</td>
<td align="center">35.99</td>
<td align="center">22.65</td>
<td align="center">&#x2212;31.98</td>
<td align="center">&#x2212;20.13</td>
</tr>
<tr>
<td align="center">March</td>
<td align="center">221.24</td>
<td align="center">184.29</td>
<td align="center">188.67</td>
<td align="center">36.95</td>
<td align="center">20.05</td>
<td align="center">32.57</td>
<td align="center">17.26</td>
<td align="center">&#x2212;4.38</td>
<td align="center">&#x2212;2.32</td>
</tr>
<tr>
<td align="center">April</td>
<td align="center">205.47</td>
<td align="center">184.84</td>
<td align="center">168.19</td>
<td align="center">20.63</td>
<td align="center">11.16</td>
<td align="center">37.28</td>
<td align="center">22.17</td>
<td align="center">16.65</td>
<td align="center">9.90</td>
</tr>
<tr>
<td align="center">May</td>
<td align="center">180.94</td>
<td align="center">164.67</td>
<td align="center">158.90</td>
<td align="center">16.27</td>
<td align="center">9.88</td>
<td align="center">22.04</td>
<td align="center">19.87</td>
<td align="center">5.77</td>
<td align="center">3.63</td>
</tr>
<tr>
<td align="center">June</td>
<td align="center">176.55</td>
<td align="center">160.70</td>
<td align="center">149.19</td>
<td align="center">15.85</td>
<td align="center">9.87</td>
<td align="center">27.36</td>
<td align="center">18.34</td>
<td align="center">11.51</td>
<td align="center">7.72</td>
</tr>
<tr>
<td align="center">July</td>
<td align="center">153.75</td>
<td align="center">141.29</td>
<td align="center">134.7</td>
<td align="center">12.46</td>
<td align="center">8.82</td>
<td align="center">19.05</td>
<td align="center">14.14</td>
<td align="center">6.59</td>
<td align="center">4.89</td>
</tr>
<tr>
<td align="center">August</td>
<td align="center">139.77</td>
<td align="center">136.43</td>
<td align="center">129.76</td>
<td align="center">3.34</td>
<td align="center">2.45</td>
<td align="center">10.01</td>
<td align="center">7.71</td>
<td align="center">6.67</td>
<td align="center">5.114</td>
</tr>
<tr>
<td align="center">September</td>
<td align="center">156.38</td>
<td align="center">151.45</td>
<td align="center">150.18</td>
<td align="center">4.93</td>
<td align="center">3.26</td>
<td align="center">6.20</td>
<td align="center">4.13</td>
<td align="center">1.27</td>
<td align="center">0.84</td>
</tr>
<tr>
<td align="center">October</td>
<td align="center">220.16</td>
<td align="center">205.00</td>
<td align="center">190.15</td>
<td align="center">15.16</td>
<td align="center">7.40</td>
<td align="center">30.01</td>
<td align="center">15.78</td>
<td align="center">14.85</td>
<td align="center">7.81</td>
</tr>
<tr>
<td align="center">November</td>
<td align="center">273.52</td>
<td align="center">235.89</td>
<td align="center">242.89</td>
<td align="center">37.63</td>
<td align="center">15.95</td>
<td align="center">30.63</td>
<td align="center">12.61</td>
<td align="center">&#x2212;7.00</td>
<td align="center">&#x2212;2.88</td>
</tr>
<tr>
<td align="center">December</td>
<td align="center">332.54</td>
<td align="center">370.11</td>
<td align="center">267.75</td>
<td align="center">&#x2212;37.56</td>
<td align="center">&#x2212;10.15</td>
<td align="center">64.79</td>
<td align="center">24.20</td>
<td align="center">102.36</td>
<td align="center">38.23</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The monthly variation trend of NO<sub>2</sub> TVCDs in 2019&#x2013;2021 is similar, showing a relatively high concentration in winter and spring, and a relatively low concentration in summer. The NO<sub>2</sub> TVCDs increases in autumn and winter mainly because of more energy consumption. Starting October 1, heating will begin in northern China as temperatures drop, which will increase coal consumption and lead to higher emissions. Meanwhile, the Chinese traditional festival Spring Festival usually falls at the end of January or the beginning of February, people have a long holiday from the Spring Festival, during which some enterprises will stop production or reduce production, and pollutant emission will decrease, resulting in a decrease in the concentration of NO<sub>2</sub> TVCDs in February. This phenomenon is also been interpreted as &#x201c;Spring Festival effect&#x201d;.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>COVID-19, which began at the end of 2019, had lasted for 2&#xa0;years by the end of 2021. COVID-19 has affected China&#x2019;s industrial production and people&#x2019;s travel and lives. The impact of COVID-19 in China can be indirectly reflected through the temporal&#x2013;spatial distribution and variation of tropospheric NO<sub>2</sub>. Meanwhile, the industrial structure changes and environmental policies have also had an impact on NO<sub>2</sub> emissions. The prevention and control of atmospheric pollution work carried out by the Ministry of Ecology and Environment from June to September 2020 has contributed to atmospheric environmental governance. The specific impact analysis is explained in the following subsections.</p>
<sec id="s4-1">
<title>4.1 Analysis of the influence of industrial production on NO<sub>2</sub>
</title>
<sec id="s4-1-1">
<title>4.1.1 Economic growth and NO<sub>2</sub> TVCDs</title>
<p>The GDP data from the National Bureau of Statistics of China showed that China is in a &#x201c;three-two-one&#x201d; industrial pattern at present. The tertiary industry is gradually moving into a dominant position and the proportion is steadily increasing, while the proportion of the secondary industry is decreasing as the added value is gradually increasing. China&#x2019;s structure of energy consumption is characterized by rich coal, poor oil, and less gas. The secondary industry has a strong energy consumption capacity, so it has a significant impact on environmental pollution.</p>
<p>During the &#x201c;11th Five-Year plan period of China&#x201d; (2006&#x2013;2010), Control Total of air pollutants were carried on, there was no definite bounded target for nitrogen oxides (NOx) emission, the China NO<sub>2</sub> TVCDs from OMI increased year by year (<xref ref-type="bibr" rid="B47">Zhou et al., 2016b</xref>). During the &#x201c;12th Five-Year Plan period of China&#x201d; (2011&#x2013;2015), NOx emission reduction targets were specified, and the importance of atmospheric environment protection reached an unprecedented stage (<xref ref-type="bibr" rid="B50">Zhu, 2018</xref>).</p>
<p>With the growth of economy, NO<sub>2</sub> TVCDs showed an obvious downward trend from 2012 to 2020. During this period, crude oil consumption continued to grow, and coal consumption was higher than in 2011 (<xref ref-type="table" rid="T4">Table 4</xref>; <xref ref-type="fig" rid="F7">Figure 7</xref>; <xref ref-type="app" rid="app2">Appendix B</xref>). A boom in desulphurization, denitrification and dust removal in key industries was opened, and the standard of waste gas discharge fees was greatly increased from 2012. China&#x2019;s economic growth is no longer positively correlated with NO<sub>2</sub> TVCDs from 2012. Desulphurization, denitrification and other end of pipe control measures in key industries may play an important role in it.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>China energy consumption from 2009 to 2021.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Time</th>
<th align="center">Total energy consumption (10,000 tons of standard coal))</th>
<th align="center">Coal consumption (10,000 tons)</th>
<th align="center">Crude oil consumption (10,000 tons)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">2009</td>
<td align="center">336126</td>
<td align="center">325002.93</td>
<td align="center">38128.59</td>
</tr>
<tr>
<td align="center">2010</td>
<td align="center">360648</td>
<td align="center">349008.26</td>
<td align="center">42874.55</td>
</tr>
<tr>
<td align="center">2011</td>
<td align="center">387043</td>
<td align="center">388961.1</td>
<td align="center">43965.84</td>
</tr>
<tr>
<td align="center">2012</td>
<td align="center">402138</td>
<td align="center">411726.9</td>
<td align="center">46678.92</td>
</tr>
<tr>
<td align="center">2013</td>
<td align="center">416913</td>
<td align="center">424425.94</td>
<td align="center">48652.15</td>
</tr>
<tr>
<td align="center">2014</td>
<td align="center">428334</td>
<td align="center">413633</td>
<td align="center">51596.95</td>
</tr>
<tr>
<td align="center">2015</td>
<td align="center">434113</td>
<td align="center">399834</td>
<td align="center">54788.28</td>
</tr>
<tr>
<td align="center">2016</td>
<td align="center">441492</td>
<td align="center">388820</td>
<td align="center">57125.93</td>
</tr>
<tr>
<td align="center">2017</td>
<td align="center">455827</td>
<td align="center">391403</td>
<td align="center">59402.17</td>
</tr>
<tr>
<td align="center">2018</td>
<td align="center">471925</td>
<td align="center">397452</td>
<td align="center">63004.33</td>
</tr>
<tr>
<td align="center">2019</td>
<td align="center">487488</td>
<td align="center">401915</td>
<td align="center">67268.27</td>
</tr>
<tr>
<td align="center">2020</td>
<td align="center">498314</td>
<td align="center">404860</td>
<td align="center">69477.14</td>
</tr>
<tr>
<td align="center">2021</td>
<td align="center">525896</td>
<td align="center">-</td>
<td align="center">-</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>Annual trends of NO<sub>2</sub> TVCDs and the primary, secondary, and tertiary industry value added of China from 2009 to 2021.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g007.tif"/>
</fig>
</sec>
<sec id="s4-1-2">
<title>4.1.2 COVID-19 and NO<sub>2</sub> TVCDs</title>
<p>In December 2019, COVID-19 broke out in Wuhan, and spread quickly across the country. The government of China carried out lockdown measurements on January 2020 in Wuhan to prevent the further spread of the COVID-19, and these measurements extending rapidly to other provinces (<xref ref-type="bibr" rid="B32">Tian et al., 2020</xref>; <xref ref-type="bibr" rid="B49">Zhou et al., 2021</xref>). The public transport in Wuhan was shut down from January 23 to 8 April 2020. These measurements had great impact on transportation and economic activities (<xref ref-type="bibr" rid="B1">Ali et al., 2021</xref>).</p>
<p>The monthly average NO<sub>2</sub> TVCDs of China from January to March 2020 decreased compared to 2019, NO<sub>2</sub> TVCDs in February decreased to an extremely low level in February (<xref ref-type="table" rid="T3">Table 3</xref>). The NO<sub>2</sub> TVCDs gradually reached or exceeded the level of the same period of 2019 from April 2020. At the same time, the growth rate of GDP in the first quarter of 2020 showed a negative growth for the only time from 2009 to 2021 (<xref ref-type="fig" rid="F8">Figure 8</xref>), and the increment of GDP_current in the second quarter of 2020 decreased compared to other years. These are consistent with lockdowns for COVID-19.</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>GDP_current quarter of China from 2009 to 2021.</p>
</caption>
<graphic xlink:href="fenvs-12-1267627-g008.tif"/>
</fig>
<p>COVID-19 affected the tropospheric NO<sub>2</sub> mainly by impacting industrial production and transportation trip, while it has little impact on domestic combustion. It might even boost domestic combustion as people spend more time at home. Therefore, the COVID-19 pandemic has a greater impact on developed cities with high industrial activity and high population density than cities with smaller populations and less industrial activity.</p>
<p>The impact of COVID-19 was mainly concentrated in the first quarter of 2020. During this period, The NO<sub>2</sub> TVCDs of 2020 decreased in Beijing-Tianjin-Hebei and the surrounding &#x201c;2 &#x2b; 26&#x201d;cities, Fen-Wei plains, and the Pearl River Delta areas, especially in Beijing, Shanghai and Wuhan (More developed city) compared to 2019 (<xref ref-type="table" rid="T2">Table 2</xref>; <xref ref-type="fig" rid="F4">Figure 4</xref>). The traffic, transportation, and industrial production of densely populated cities were busy before COVID-19, and declined when affected by COVID-19, while other areas were less affected. After the initial outbreak, with the implementation of normalized prevention and control, COVID-19 mainly affected people&#x2019;s transportation to a certain extent, while industrial production and people&#x2019;s lives were almost unaffected. When China completely lifts the lockdowns and resumes large-scale industrial production, NO<sub>2</sub> will catch up with or even surpass the level before the COVID-19.</p>
</sec>
</sec>
<sec id="s4-2">
<title>4.2 Analysis of the impact of transportation on NO<sub>2</sub>
</title>
<p>NO<sub>2</sub> mainly comes from the high-temperature combustion of fuels such as petroleum or coal. The sources of NO<sub>2</sub> in cities are mainly from transportation and industrial production emissions. <xref ref-type="table" rid="T5">Table 5</xref> shows that the passenger turnover in 2020 and 2021 decreased by 44.11% and 45.54% compared with 2019. In 2020, the freight turnover decreased by 5.99% compared with 2019, while it increased by 4.30% in 2021 compared with 2019. The normalized prevention and control of the COVID-19 reduced passenger travel, but it had less impact on freight turnover because of the need to ensure the daily needs of residents and industrial production were met. At the same time, China&#x2019;s gasoline apparent consumption decreased by 1.88% and 7.17% in 2021 and 2020 compared with 2019, respectively. The decline of transportation played a positive role on the decline of NO<sub>2</sub> TVCDs.</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>In 2019&#x2013;2021, China&#x2019;s freight turnover, passenger turnover, gasoline production, gasoline apparent consumption, raw coal production, gas apparent consumption, and total energy consumption statistics.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Date</th>
<th align="center">2021</th>
<th align="center">2020</th>
<th align="center">2019</th>
<th align="center">Comparison between 2021 and 2020 (%)</th>
<th align="center">Comparison between 2021 and 2019 (%)</th>
<th align="center">Comparison between 2020 and 2019 (%)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Freight turnover (1&#x2a;10<sup>9</sup> ton-kilometer)</td>
<td align="center">218134</td>
<td align="center">196618.3</td>
<td align="center">209137</td>
<td align="center">10.94</td>
<td align="center">4.30</td>
<td align="center">&#x2212;5.99</td>
</tr>
<tr>
<td align="center">Passenger turnover (1&#x2a;10<sup>9</sup> ton-kilometer)</td>
<td align="center">19758.15</td>
<td align="center">19251.35</td>
<td align="center">35349.06</td>
<td align="center">2.63</td>
<td align="center">&#x2212;44.11</td>
<td align="center">&#x2212;45.54</td>
</tr>
<tr>
<td align="center">Gasoline output (1&#x2a;10<sup>4</sup> tons)</td>
<td align="center">15457</td>
<td align="center">13172</td>
<td align="center">14121</td>
<td align="center">17.35</td>
<td align="center">9.46</td>
<td align="center">&#x2212;6.72</td>
</tr>
<tr>
<td align="center">Apparent consumption of gasoline (1&#x2a;10<sup>4</sup> tons)</td>
<td align="center">12282</td>
<td align="center">11620</td>
<td align="center">12517</td>
<td align="center">5.70</td>
<td align="center">&#x2212;1.88</td>
<td align="center">&#x2212;7.17</td>
</tr>
<tr>
<td align="center">Raw coal output (1&#x2a;10<sup>8</sup> tons)</td>
<td align="center">40.7</td>
<td align="center">39</td>
<td align="center">38.5</td>
<td align="center">4.36</td>
<td align="center">5.71</td>
<td align="center">1.30</td>
</tr>
<tr>
<td align="center">Apparent consumption of natural gas (1&#x2a;10<sup>8</sup> cubic meters)</td>
<td align="center">3728</td>
<td align="center">3259.1</td>
<td align="center">3064</td>
<td align="center">14.36</td>
<td align="center">21.67</td>
<td align="center">6.37</td>
</tr>
<tr>
<td align="center">Total energy consumption (1&#x2a;10<sup>4</sup> tons of standard coal)</td>
<td align="center">524000</td>
<td align="center">498314</td>
<td align="center">487488</td>
<td align="center">5.15</td>
<td align="center">7.49</td>
<td align="center">2.22</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s4-3">
<title>4.3 Analysis of the impact of atmospheric environmental management policy on NO<sub>2</sub>
</title>
<p>Air pollution prevention and control has always been an important part of China&#x2019;s environmental protection, and keep improving with the evolution of the main air environment problem appeared in the process of economic development. Over the past decades, China has done a lot of work in air pollution prevention and control, and achieved remarkable results (<xref ref-type="bibr" rid="B5">Chai, 2020</xref>).</p>
<p>The new &#x201c;Ambient air quality standards&#x201d; was issued in 2012, and &#x201c;Action Plan for Prevention and Control of Air Pollution&#x201d; was issued in 2013. &#x201c;Environmental Protection Supervision Program (Trial)&#x201d; and &#x201c;Law of the People&#x2019;s Republic of China on the Prevention and Control of Atmospheric Pollution (2015)&#x201d; in 2015, all these have contributed to the improvement of air quality (<xref ref-type="bibr" rid="B5">Chai, 2020</xref>).</p>
<p>China has done a lot of work in air pollution control and air quality management, especially in some typical industrial regions (&#x201c;2 &#x2b; 26&#x201d;cities in Beijing-Tianjin-Hebei and surrounding areas, Yangtze River Delta region, Fen-Wei Plains and other regions) (<xref ref-type="bibr" rid="B5">Chai, 2020</xref>). These policies include fuel desulfurization and denitrification measures, the elimination and upgrading of waste industrial facilities, the improvement of environmental protection related technologies, the use of clean energy, etc. (<xref ref-type="table" rid="T6">Table 6</xref>), and these measures have played an important role in the NO<sub>2</sub> treatment process, especially in industrial cities.</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>List of major policies for air pollution prevention and control in China.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Index</th>
<th align="center">Date</th>
<th align="center">Main policy</th>
<th align="center">The main measures</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">1</td>
<td align="center">2012.2</td>
<td align="center">The new &#x201c;Ambient air quality standards (GB3095&#x2014;2012)&#x201d;</td>
<td align="center">The limit of NO<sub>2</sub> concentration emission has been tightened, and the validity regulations of monitoring data statistics and the analysis methods of air pollutants have been updated</td>
</tr>
<tr>
<td align="center">2</td>
<td align="center">2013.6</td>
<td align="center">&#x201c;Action Plan for Prevention and Control of Air Pollution&#x201d;</td>
<td align="center">We will comprehensively improve small coal-fired boilers and speed up the upgrading of key industries for desulfurization, denitrification and dust removal. Improve fuel quality and eliminate yellow label vehicles within a time limit</td>
</tr>
<tr>
<td align="center">3</td>
<td align="center">2013.9</td>
<td align="center">Action Plan for the prevention and control of air pollution</td>
<td align="center">It strengthens comprehensive management, reduce multi-pollutant emissions, and start a boom in desulfurization, denitration and dust removal in key industries</td>
</tr>
<tr>
<td align="center">4</td>
<td align="center">2014.9</td>
<td align="center">Adjusting the collection standards of sewage charges and other related issues</td>
<td align="center">Fees for waste gas and pollution were significantly raised, and law enforcement on environmental protection was strengthened</td>
</tr>
<tr>
<td align="center">5</td>
<td align="center">2016.12</td>
<td align="center">Comprehensive work plan for energy conservation and emission reduction during the 13th 5-year plan period</td>
<td align="center">Targets for controlling air pollution have been significantly raised</td>
</tr>
<tr>
<td align="center">6</td>
<td align="center">2017.9</td>
<td align="center">&#x201c;13th 5-year plan&#x201d; Volatile organic compound pollution prevention and control work plan</td>
<td align="center">The prevention and control of VOCs pollution has become a new key area of atmospheric governance, which focusing on the treatment of VOCs and their precursors</td>
</tr>
<tr>
<td align="center">7</td>
<td align="center">2018.7</td>
<td align="center">Three-year action plan to win the battle for the protection of the blue sky</td>
<td align="center">The main quantitative targets for air pollution prevention and control by 2020 have been clarified</td>
</tr>
<tr>
<td align="center">8</td>
<td align="center">2020.3</td>
<td align="center">On the construction of a modern environmental governance system</td>
<td align="center">We will strengthen independent innovation in key environmental technology products, and accelerate the improvement of technology and equipment in environmental protection industries</td>
</tr>
<tr>
<td align="center">9</td>
<td align="center">2021.3</td>
<td align="center">The 14th 5-year plan</td>
<td align="center">The coordinated control of PM2.5 and ozone</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The NO<sub>2</sub> TVCDs of &#x201c;2 &#x2b; 26&#x201d;cities and Fen-Wei plains (95 cities) decreased by 2.57% and 5.36% in 2020 compared to 2019 respectively, which exceeded the China average decrease (<xref ref-type="table" rid="T2">Table2</xref>).</p>
<p>
<xref ref-type="fig" rid="F7">Figure 7</xref> shows that the secondary industry value added of China grew steadily from 2009 to 2021, while the NO<sub>2</sub> TVCDs of China decreased from 2011 to 2019. The tertiary industry value added exceeded the secondary industry value added from 2012. This indicates that the effects of national atmospheric environmental management policy began to show in 2012, the secondary industry value added continued to grow with NO<sub>2</sub> TVCDs showed a downward trend from 2012 to 2019, especially in 2014, 2015, and 2018.</p>
</sec>
<sec id="s4-4">
<title>4.4 Analysis of other influencing factors</title>
<p>The meteorological influences may be twofold (<xref ref-type="bibr" rid="B8">Fan et al., 2021</xref>). Meteorological conditions can contribute to the pollution formation, especially in winter in northern China (<xref ref-type="bibr" rid="B13">Li et al., 2018</xref>; <xref ref-type="bibr" rid="B38">Wang et al., 2019</xref>). Meanwhile, the transport of clean air provided by meteorological conditions can reduce the concentration of atmospheric pollutants (<xref ref-type="bibr" rid="B13">Li et al., 2018</xref>; <xref ref-type="bibr" rid="B38">Wang et al., 2019</xref>). Precipitation and wind speed have the greatest impact on air pollutants, among other meteorological factors. Many studies have shown that there is a negative correlation between NO<sub>2</sub> concentration and precipitation or wind speed in China (<xref ref-type="bibr" rid="B46">Zhou et al., 2016a</xref>; <xref ref-type="bibr" rid="B13">Li et al., 2018</xref>; <xref ref-type="bibr" rid="B38">Wang et al., 2019</xref>).</p>
<p>According to the Meteorological center data of China Meteorological Administration (<ext-link ext-link-type="uri" xlink:href="http://data.cma.cn/">http://data.cma.cn/</ext-link>), China has entered an extreme warm and humid pattern in recent 10&#xa0;years. The last 10&#xa0;years (2012&#x2013;2021) were the warmest and wettest on record in China. As it gets hotter, so does the precipitation. Meteorological conditions are generally conducive to the diffusion of air pollutants. Meteorological conditions played a positive role in NO<sub>2</sub> decline during 2012&#x2013;2021.</p>
<p>Soil is also an important emission source of atmospheric NOx. Some studies have shown that the nitrogen oxide emissions of soil in North China Plain in summer can reach 20% of the man-made nitrogen oxide emissions (<xref ref-type="bibr" rid="B18">Lu et al., 2021</xref>). According to the emission inventory, the soil NOx emissions contributed to 17.3% of the total emissions during 2017 (<xref ref-type="bibr" rid="B25">Shen et al., 2022</xref>). This paper did not conduct an in-depth study on the emission of NOx from agricultural activities during 2009&#x2013;2021. The change of NO<sub>2</sub> TVCDs in this paper also included the influence of soil emission.</p>
</sec>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>This study monitored the tropospheric NO<sub>2</sub> column concentration of China from 2009 to 2021 based on OMI and TROPOMI product, and analyzed the factors related to the change of NO<sub>2</sub>, including COVID-19, transportation, the secondary/tertiary industry value added, apparent consumption of raw coal and gasoline, and GDP.</p>
<p>The following conclusions can be drawn:<list list-type="simple">
<list-item>
<p>(1) The effects of national atmospheric environmental management policy began to show in 2012, the secondary industry value added continued to grow with NO<sub>2</sub> TVCDs showed a downward trend from 2012 to 2020, especially in 2014, 2015and 2018. China&#x2019;s economic growth is no longer positively correlated with NO<sub>2</sub> TVCDs from 2012. Desulphurization, denitrification and other end of pipe control measures in key industries may play an important role in it.</p>
</list-item>
<list-item>
<p>(2) A series of measures were carried out especially in some typical industrial regions (&#x201c;2 &#x2b; 26&#x201d;cities in Beijing-Tianjin -Hebei and surrounding areas, Yangtze River Delta region, Fen-Wei Plains and other regions), which may reduce NO<sub>2</sub> emissions in these areas. The national air pollution prevention and control work is more obvious in industrial cities.</p>
</list-item>
<list-item>
<p>(3) COVID-19 mainly affected the first quarter of 2020, leading to significantly reduced transportation and industrial production. The NO<sub>2</sub> TVCDs reached a very low value, and the sequential growth rate of GDP had a rare negative growth period.</p>
</list-item>
</list>
</p>
<p>This paper only analyzes the overall changes of NO<sub>2</sub> TVCDs and the influences of national policies, industrial production and transportation, without removing the influences of meteorological and soil emission factors. Future work can combine meteorological and soil emission factors to quantitatively analyze industrial production emissions and transportation emissions.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="s7">
<title>Author contributions</title>
<p>YZ: Software, Validation, Writing&#x2013;review and editing. LC: Validation, Investigation, Writing&#x2013;original draft. WG: Investigation, Writing&#x2013;original draft, Resources. CZ: Methodology, Writing&#x2013;original draft. ZL: Supervision, Writing&#x2013;review and editing.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Major Projects of High Resolution Earth Observation Systems of National Science and Technology (05-Y30B01-9001-19/20-3).</p>
</sec>
<ack>
<p>The GDP, turnover of freight and passenger, energy consumption data used in this study are all from the National Bureau of Statistics of the People&#x2019;s Republic of China. TROPOMI tropospheric NO<sub>2</sub> column concentration products are provided by TEMIs (tropospheric emission monitoring Internet service).</p>
</ack>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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<app-group>
<app id="app1">
<title>Appendix A</title>
<p>There are 105 cities in the five regions, including 28 cities in Beijing-Tianjin-Hebei and surrounding &#x201c;2 &#x2b; 26&#x201d;cities, 41 cities in the Yangtze River Delta, 11 cities in the Fen-Wei plains, 9 cities in the Pearl River Delta, and 16 cities in Cheng-Yu district.</p>
<table-wrap id="audT1" position="float">
<table>
<thead valign="top">
<tr>
<th align="center">Region</th>
<th align="center">Province (autonomous regions and cities)</th>
<th align="center">City</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="6" align="center">Beijing-Tianjin-Hebei and its surrounding &#x201c;2 &#x2b; 26&#x2033;cities (28 in total)</td>
<td align="center">Beijing</td>
<td align="center">Beijing</td>
</tr>
<tr>
<td align="center">Tianjin</td>
<td align="center">Tianjin</td>
</tr>
<tr>
<td align="center">Hebei</td>
<td align="center">Shijiazhuang, Tangshan, Handan, Xingtai, Baoding, Cangzhou, Langfang and Hengshui</td>
</tr>
<tr>
<td align="center">Shanxi</td>
<td align="center">Taiyuan, Yangquan, Changzhi and Jincheng</td>
</tr>
<tr>
<td align="center">Shandong</td>
<td align="center">There are seven cities in Jinan, Zibo, Jining, Dezhou, Liaocheng, Binzhou and Heze</td>
</tr>
<tr>
<td align="center">Henan</td>
<td align="center">There are 7 cities in Zhengzhou, Kaifeng, Anyang, Hebi, Xinxiang, Jiaozuo and Puyang</td>
</tr>
<tr>
<td rowspan="4" align="center">Yangtze River Delta (41 in total)</td>
<td align="center">Shanghai</td>
<td align="center">Shanghai</td>
</tr>
<tr>
<td align="center">Jiangsu</td>
<td align="center">Nanjing, Wuxi, Xuzhou, Changzhou, Suzhou, Nantong, Lianyungang, Huai&#x2019;an, Yancheng.There are 13 cities in Yangzhou, Zhenjiang, Taizhou and Suqian</td>
</tr>
<tr>
<td align="center">Zhejiang</td>
<td align="center">Hangzhou, Ningbo, Wenzhou, Shaoxing, Huzhou, Jiaxing, Jinhua, Quzhou, Taizhou, Lishui and Zhoushan</td>
</tr>
<tr>
<td align="center">Anhui</td>
<td align="center">Hefei, Wuhu, Bengbu, Huainan, Ma&#x2019;anshan, Huaibei, Tongling, Anqing, Huangshan, Fuyang, Suzhou, Chuzhou, Lu&#x2019;an, Xuancheng, Chizhou and Bozhou</td>
</tr>
<tr>
<td rowspan="3" align="center">Fen-Wei plains (11 in total)</td>
<td align="center">Shanxi</td>
<td align="center">Jinzhong, Linfen, Luliang and Yuncheng</td>
</tr>
<tr>
<td align="center">Henan</td>
<td align="center">Luoyang and Sanmenxia</td>
</tr>
<tr>
<td align="center">Shaanxi</td>
<td align="center">Xi&#x2019;an, Baoji, Tongchuan, Weinan and Xianyang</td>
</tr>
<tr>
<td align="center">Pearl River Delta (9 in total)</td>
<td align="center">Guangdong</td>
<td align="center">Guangzhou, Shenzhen, Zhuhai, Foshan, Jiangmen, Zhaoqing, Huizhou, Dongguan and Zhongshan</td>
</tr>
<tr>
<td rowspan="2" align="center">Cheng-Yu district (16 in total)</td>
<td align="center">Chongqing</td>
<td align="center">Chongqing</td>
</tr>
<tr>
<td align="center">Sichuan</td>
<td align="center">Chengdu, Deyang, Mianyang, Leshan, Meishan, Ya&#x2019;an, Ziyang, Nanchong, Guang&#x2019;an and Dazhou</td>
</tr>
</tbody>
</table>
</table-wrap>
</app>
<app id="app2">
<title>Appendix B</title>
<p>China NO<sub>2</sub> TVCD of OMI from 2009 to 2021.</p>
</app>
</app-group>
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