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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Public Health</journal-id>
<journal-title>Frontiers in Public Health</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Public Health</abbrev-journal-title>
<issn pub-type="epub">2296-2565</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpubh.2023.1250572</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Public Health</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>China&#x2019;s air quality improvement strategy may already be having a positive effect: evidence based on health risk assessment</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Xu</surname>
<given-names>Xianmang</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<xref rid="aff3" ref-type="aff"><sup>3</sup></xref>
<xref rid="c001" ref-type="corresp"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2024556/overview"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/project-administration/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Wen</given-names>
</name>
<xref rid="aff4" ref-type="aff"><sup>4</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shi</surname>
<given-names>Xiaofeng</given-names>
</name>
<xref rid="aff4" ref-type="aff"><sup>4</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Su</surname>
<given-names>Zhi</given-names>
</name>
<xref rid="aff5" ref-type="aff"><sup>5</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cheng</surname>
<given-names>Wei</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wei</surname>
<given-names>Yinuo</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ma</surname>
<given-names>He</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Tinglong</given-names>
</name>
<xref rid="aff1" ref-type="aff"><sup>1</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Zhenhua</given-names>
</name>
<xref rid="aff2" ref-type="aff"><sup>2</sup></xref>
<xref rid="c002" ref-type="corresp"><sup>&#x002A;</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Heze Branch, Biological Engineering Technology Innovation Center of Shandong Province, Qilu University of Technology (Shandong Academy of Sciences)</institution>, <addr-line>Heze</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences)</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Institute of Atmospheric Sciences, Fudan University</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Clinical Medicine, Heze Medical College</institution>, <addr-line>Heze</addr-line>, <country>China</country></aff>
<aff id="aff5"><sup>5</sup><institution>Heze Ecological Environment Monitoring Center of Shandong Province</institution>, <addr-line>Heze</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0007"><p>Edited by: Ghulam Mujtaba Kayani, Hubei University of Economics, China</p></fn>
<fn fn-type="edited-by" id="fn0008"><p>Reviewed by: Manthar Ali Mallah, Zhengzhou University, China; Mohd Faiz Ibrahim, Ministry of Health, Malaysia</p></fn>
<corresp id="c001">&#x002A;Correspondence: Xianmang Xu, <email>xuxianmang168@qlu.edu.cn</email></corresp>
<corresp id="c002">Zhenhua Wang, <email>wangzhh@qlu.edu.cn</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>10</day>
<month>10</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>11</volume>
<elocation-id>1250572</elocation-id>
<history>
<date date-type="received">
<day>30</day>
<month>06</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>15</day>
<month>09</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2023 Xu, Zhang, Shi, Su, Cheng, Wei, Ma, Li and Wang.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Xu, Zhang, Shi, Su, Cheng, Wei, Ma, Li and Wang</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>Aiming to investigate the health risk impact of PM<sub>2.5</sub> pollution on a heavily populated province of China. The exposure response function was used to assess the health risk of PM<sub>2.5</sub> pollution. Results shows that the total number of premature deaths and diseases related to PM<sub>2.5</sub> pollution in Shandong might reach 159.8 thousand people based on the new WHO (2021) standards. The health effects of PM<sub>2.5</sub> pollution were more severe in men than in women. Five of the 16 cities in Shandong had higher health risks caused by PM<sub>2.5</sub> pollution, including LinYi, HeZe, JiNing, JiNan, and WeiFang. PM<sub>2.5</sub> pollution resulted in nearly 7.4 billions dollars in healthy economic cost, which accounted for 0.57% of GDP in Shandong in 2021. HeZe, LiaoCheng, ZaoZhuang, and LinYi were the cities where the health economic loss was more than 1% of the local GDP, accounted for 1.30, 1.26, 1.08, and 1.04%. Although the more rigorous assessment criteria, the baseline concentration was lowered by 30&#x2009;&#x03BC;g/m<sup>3</sup> compared to our previous study, there was no significant increase in health risks and economic losses. China&#x2019;s air quality improvement strategy may already be having a positive effect.</p>
</abstract>
<kwd-group>
<kwd>health risk</kwd>
<kwd>economic loss</kwd>
<kwd>PM<sub>2.5</sub> pollution</kwd>
<kwd>exposure</kwd>
<kwd>prevention and control strategies</kwd>
</kwd-group>
<contract-num rid="cn1">FDLAP21001</contract-num>
<contract-num rid="cn2">ZR2023QB037</contract-num>
<contract-num rid="cn3">2022CXPT053</contract-num>
<contract-num rid="cn4">2022PX038, 2022GH020</contract-num>
<contract-num rid="cn5">202213</contract-num>
<contract-sponsor id="cn1">Opening Project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3)</contract-sponsor>
<contract-sponsor id="cn2">Shandong Provincial Natural Science Foundation<named-content content-type="fundref-id">10.13039/501100007129</named-content></contract-sponsor>
<contract-sponsor id="cn3">Key R&#x0026;D Program of Shandong Province, China</contract-sponsor>
<contract-sponsor id="cn4">Pilot project of integration of science, education and production of Qilu University of Technology (Shandong Academy of Sciences)</contract-sponsor>
<contract-sponsor id="cn5">Project of Shandong Society for Environmental</contract-sponsor>
<counts>
<fig-count count="2"/>
<table-count count="5"/>
<equation-count count="4"/>
<ref-count count="66"/>
<page-count count="10"/>
<word-count count="8146"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Environmental health and Exposome</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<title>Introduction</title>
<p>Air pollution and population health have always been hot topics in the field of environmental research (<xref ref-type="bibr" rid="ref1 ref2 ref3 ref4">1&#x2013;4</xref>). In the past decades, air pollution has caused a series of serious health hazards to people in China (<xref ref-type="bibr" rid="ref5 ref6 ref7 ref8">5&#x2013;8</xref>). As one of the main pollutants of air pollution, fine particulate matters (PM<sub>2.5</sub>) contains complex chemical components which including various toxic substances (<xref ref-type="bibr" rid="ref9 ref10 ref11">9&#x2013;11</xref>). Because of its diminutive size, PM<sub>2.5</sub> can enter in the respiratory tract and lungs (<xref ref-type="bibr" rid="ref12">12</xref>). Once some toxic substances enter the human bloodstream, they may increase the burden on the heart (<xref ref-type="bibr" rid="ref13">13</xref>). Long-term exposure to high concentrations of PM<sub>2.5</sub> will increase the health risk of the population, especially the respiratory diseases and cardiovascular diseases (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref15">15</xref>). It also increases health care costs in related areas (<xref ref-type="bibr" rid="ref16">16</xref>).</p>
<p>In previous studies, respiratory disease, cardiovascular disease, and lung disease were typically used as the health endpoints of health risk assessment (<xref ref-type="bibr" rid="ref14 ref15 ref16 ref17">14&#x2013;17</xref>). In some studies, asthma, acute bronchitis and chronic bronchitis are also part of the evaluation system (<xref ref-type="bibr" rid="ref7">7</xref>, <xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref19">19</xref>). Some scholars use country&#x2019;s air quality standards as health guidelines (<xref ref-type="bibr" rid="ref20">20</xref>). In other studies, the World Health Organization (WHO) air quality guidelines are generally used as the baseline concentration for calculation. No matter which standard is adopted, it reflects people&#x2019;s concern for environmental safety. That focus has been growing in recent years.</p>
<p>In March 2021, the &#x201C;14th Five-Year Plan for National Economic and Social Development of the People&#x2019;s Republic of China and the Outline of Long-term Goals for 2035&#x201D; offered to intensify the battle against pollution and basically eliminate heavy pollution days. In October 2021, 10 ministries and commissions including the Ministry of Ecology and Environment and the governments of seven provinces (municipalities) including Shandong jointly issued the &#x201C;Plan for Comprehensive Control of Air Pollution in Autumn and Winter 2021&#x2013;2022.&#x201D; 13 of the 16 cities in Shandong were included in the strategic control regions. In the &#x201C;Action Plan for the Treatment of New Pollutants (2022)&#x201D; issued by the General Office of the State Council, environmental health risk prevention has also been put at the heart of the case. Reducing the health risks and costs of PM<sub>2.5</sub> pollution is a growing concern. As one of the most polluted areas in North China, Shandong is still facing a severe situation of air pollution prevention and control (<xref ref-type="bibr" rid="ref21">21</xref>, <xref ref-type="bibr" rid="ref22">22</xref>). And the health and economic costs caused by PM<sub>2.5</sub> pollution in Shandong should be made seriously.</p>
<p>As the third largest province in GDP in China, Shandong was plagued by air pollution (<xref ref-type="bibr" rid="ref23">23</xref>). Although air quality in Shandong had been improving in recent years, heavy pollution events were still common in some cities (<xref ref-type="bibr" rid="ref24 ref25 ref26">24&#x2013;26</xref>). At present, only a few developed cites in Shandong have publicly reported the health risk of PM<sub>2.5</sub>, such as Jinan and Qingdao (<xref ref-type="bibr" rid="ref27">27</xref>, <xref ref-type="bibr" rid="ref28">28</xref>). There was not any accurate data on the health cost of PM<sub>2.5</sub> pollution for the whole Shandong Province. According to the relevant studies in key regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta, the health cost of PM<sub>2.5</sub> pollution exposure might accounts for 0.3&#x2013;1.0% of the total annual GDP (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref29 ref30 ref31 ref32 ref33">29&#x2013;33</xref>). In 2021, the health cost caused by PM<sub>2.5</sub> pollution in Shandong Province was preliminarily estimated to be about 3.86&#x2013;12.88 billion dollars. On September 22, 2021, the WHO further improved the original air quality guidelines based on the conclusions of the current important reports by global scholars, and lowered the annual recommended level of PM<sub>2.5</sub> from 10&#x2009;&#x03BC;g/m<sup>3</sup> to 5&#x2009;&#x03BC;g/m<sup>3</sup>. The 24-h recommended level of PM<sub>2.5</sub> was reduced from 25&#x2009;&#x03BC;g/m<sup>3</sup> to 15&#x2009;&#x03BC;g/m<sup>3</sup>. The reduction in the health guideline concentration means a change in the original health risk assessment criteria for PM<sub>2.5</sub> exposure. It also implies that the economic cost of PM<sub>2.5</sub> exposure may have been underestimated.</p>
<p>In order to understand the PM<sub>2.5</sub> health risk in Shandong Province. In this study, the health and economic effects of PM<sub>2.5</sub> exposure in Shandong were evaluated using the new WHO guidelines as health threshold. The evaluation results were also compared with our previous study to discuss the impact of the new WHO guidelines on health risk assessment. Finally, the prevention and control strategies of air pollution in China were discussed based on the evaluation results. Therefore, this study will help clarify the health costs of PM<sub>2.5</sub> pollution and fill the gap on the health economic effects of PM<sub>2.5</sub> pollution in Shandong Province. It also provided scientific reference for the optimization of air pollution control strategy in China.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<title>Materials and methods</title>
<sec id="sec3">
<title>Location information</title>
<p>Shandong Province is situated in the North China Plain, on the east coast of China. It consists of 16 cities (<xref rid="fig1" ref-type="fig">Figure 1</xref>). It covers an area of 158,000 square kilometers and has a population of over 101.5 millions (2021). Basic data in 16 cities of Shandong Province was shown in <xref rid="tab1" ref-type="table">Table 1</xref>. The annual average concentration of PM<sub>2.5</sub> was 39&#x2009;&#x03BC;g/m<sup>3</sup>, a year-on-year improvement of 15.2% (2021). The annual average concentration of 39&#x2009;&#x03BC;g/m<sup>3</sup> was well above the new health guidelines of WHO. The Ambient Air Quality Composite Index, which takes into account the concentrations of six pollutants including PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO, and O<sub>3</sub>, is used to rank the air quality of 168 key Chinese cities. In this comprehensive index ranking of 168 key cities in China, four cities including Zibo, Liaocheng, Heze and Zaozhuang were in the bottom 20. The situation of air pollution prevention and control in Shandong province was still serious.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p><bold>(A)</bold> Shandong Province within China; <bold>(B)</bold> Cities in Shandong Province.</p>
</caption>
<graphic xlink:href="fpubh-11-1250572-g001.tif"/>
</fig>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Basic data in 16 cities of Shandong Province.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Districts</th>
<th align="center" valign="top">PM<sub>2.5</sub> annual average concentration (&#x03BC;g/m<sup>3</sup>)</th>
<th align="center" valign="top">Residents (million people)</th>
<th align="center" valign="top">Male/female ratio</th>
<th align="center" valign="top">Area (Km<sup>2</sup>)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">WeiHai</td>
<td align="center" valign="top">24</td>
<td align="char" valign="top" char=".">2.907</td>
<td align="char" valign="top" char=".">0.978</td>
<td align="center" valign="top">5,799</td>
</tr>
<tr>
<td align="left" valign="top">YanTai</td>
<td align="center" valign="top">27</td>
<td align="char" valign="top" char=".">7.102</td>
<td align="char" valign="top" char=".">0.985</td>
<td align="center" valign="top">13,864</td>
</tr>
<tr>
<td align="left" valign="top">QingDao</td>
<td align="center" valign="top">28</td>
<td align="char" valign="top" char=".">10.072</td>
<td align="char" valign="top" char=".">0.973</td>
<td align="center" valign="top">11,293</td>
</tr>
<tr>
<td align="left" valign="top">Rizhao</td>
<td align="center" valign="top">31</td>
<td align="char" valign="top" char=".">2.968</td>
<td align="char" valign="top" char=".">1.034</td>
<td align="center" valign="top">5,358</td>
</tr>
<tr>
<td align="left" valign="top">DongYing</td>
<td align="center" valign="top">36</td>
<td align="char" valign="top" char=".">2.194</td>
<td align="char" valign="top" char=".">0.986</td>
<td align="center" valign="top">8,243</td>
</tr>
<tr>
<td align="left" valign="top">WeiFang</td>
<td align="center" valign="top">38</td>
<td align="char" valign="top" char=".">9.387</td>
<td align="char" valign="top" char=".">1.015</td>
<td align="center" valign="top">16,167</td>
</tr>
<tr>
<td align="left" valign="top">JiNan</td>
<td align="center" valign="top">40</td>
<td align="char" valign="top" char=".">9.202</td>
<td align="char" valign="top" char=".">0.982</td>
<td align="center" valign="top">10,244</td>
</tr>
<tr>
<td align="left" valign="top">BinZhou</td>
<td align="center" valign="top">40</td>
<td align="char" valign="top" char=".">3.929</td>
<td align="char" valign="top" char=".">1.018</td>
<td align="center" valign="top">9,660</td>
</tr>
<tr>
<td align="left" valign="top">TaiAn</td>
<td align="center" valign="top">42</td>
<td align="char" valign="top" char=".">5.472</td>
<td align="char" valign="top" char=".">1.020</td>
<td align="center" valign="top">7,762</td>
</tr>
<tr>
<td align="left" valign="top">DeZhou</td>
<td align="center" valign="top">42</td>
<td align="char" valign="top" char=".">5.611</td>
<td align="char" valign="top" char=".">1.028</td>
<td align="center" valign="top">10,356</td>
</tr>
<tr>
<td align="left" valign="top">LinYi</td>
<td align="center" valign="top">43</td>
<td align="char" valign="top" char=".">11.018</td>
<td align="char" valign="top" char=".">1.071</td>
<td align="center" valign="top">17,191</td>
</tr>
<tr>
<td align="left" valign="top">ZaoZhuang</td>
<td align="center" valign="top">45</td>
<td align="char" valign="top" char=".">3.856</td>
<td align="char" valign="top" char=".">1.098</td>
<td align="center" valign="top">4,564</td>
</tr>
<tr>
<td align="left" valign="top">LiaoCheng</td>
<td align="center" valign="top">46</td>
<td align="char" valign="top" char=".">5.952</td>
<td align="char" valign="top" char=".">1.058</td>
<td align="center" valign="top">8,628</td>
</tr>
<tr>
<td align="left" valign="top">ZiBo</td>
<td align="center" valign="top">47</td>
<td align="char" valign="top" char=".">4.704</td>
<td align="char" valign="top" char=".">0.985</td>
<td align="center" valign="top">5,965</td>
</tr>
<tr>
<td align="left" valign="top">JiNing</td>
<td align="center" valign="top">47</td>
<td align="char" valign="top" char=".">8.358</td>
<td align="char" valign="top" char=".">1.064</td>
<td align="center" valign="top">11,187</td>
</tr>
<tr>
<td align="left" valign="top">HeZe</td>
<td align="center" valign="top">48</td>
<td align="char" valign="top" char=".">8.796</td>
<td align="char" valign="top" char=".">1.093</td>
<td align="center" valign="top">12,239</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec4">
<title>PM<sub>2.5</sub> concentration data</title>
<p>Shandong is one of the provinces with serious air pollution in north China, especially in the western part of Shandong. According to the Bulletin of Ecological Environment of Shandong Province (BEESP 2021), only four cities had PM<sub>2.5</sub> concentrations that met the II-level National Guidance Standard, including Qingdao, Yantai, Weihai, and Rizhao. The remaining 12 cities had average annual concentrations of more than 35&#x2009;&#x03BC;g/m<sup>3</sup>. All of the 16 cities failed to meet WHO health guideline. In this study, the PM<sub>2.5</sub> data was obtained from the Bulletin of Ecological Environment of Shandong Province (BEESP 2021),<xref rid="fn0001" ref-type="fn"><sup>1</sup></xref> Shandong Environmental Air Quality Status Report (SEAQSR 2021),<xref rid="fn0002" ref-type="fn"><sup>2</sup></xref> and the Official website of Shandong Department of Ecology and Environment.<xref rid="fn0003" ref-type="fn"><sup>3</sup></xref></p>
</sec>
<sec id="sec5">
<title>Population health information</title>
<p>Since population health data were difficult to obtain, the health data used in this study mainly came from the Disease and Health Status Report of Residents in Shandong Province (DHSR 2016; it can be obtained by contacting corresponding author) and the Report on Incidence and Mortality of Key Chronic Diseases in Shandong Province (RIMKCD 2018; it can be obtained by contacting corresponding author). The health cost data was obtained from the Statistical Bulletin of Health Development of Shandong Province (SBHDSP 2021).<xref rid="fn0004" ref-type="fn"><sup>4</sup></xref> Population data were obtained from the Seventh National Census (SNC 2021)<xref rid="fn0005" ref-type="fn"><sup>5</sup></xref> published in May 2021. This study also assessed the health risks of PM<sub>2.5</sub> for different genders in Shandong. The Male/Female ratio was from the public security household registration statistics in Shandong Statistical Yearbook (SSY 2022).<xref rid="fn0006" ref-type="fn"><sup>6</sup></xref> Area data was drawn from government portals.</p>
</sec>
<sec id="sec6">
<title>PM<sub>2.5</sub> health effect assessment</title>
<p>To assess the health risks of PM<sub>2.5</sub> exposure, the first step should be to correlate PM<sub>2.5</sub> concentrations with population health. Therefore, it is a critical step to determine the exposure response coefficients used in this work. In this study, all exposure response coefficients were referenced from our earlier studies and other recent relevant studies in China (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref27">27</xref>, <xref ref-type="bibr" rid="ref34 ref35 ref36">34&#x2013;36</xref>). <xref rid="tab2" ref-type="table">Table 2</xref> presents the baseline incidence for six health endpoints in Shandong Province.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Baseline incidences and exposure-response coefficients associated with 10&#x2009;&#x03BC;g&#x2009;m<sup>&#x2212;3</sup> increment of PM<sub>2.5</sub>.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Health endpoints</th>
<th align="center" valign="top" colspan="3">Incidence</th>
<th align="center" valign="top" rowspan="2">Coefficients <italic>&#x03B2;<sub>i</sub></italic> (95% CI)</th>
<th align="center" valign="top" rowspan="2">References</th>
</tr>
<tr>
<th align="center" valign="top">Male</th>
<th align="center" valign="top">Female</th>
<th align="center" valign="top">All</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">All-cause mortality</td>
<td align="char" valign="top" char=".">0.0081804</td>
<td align="char" valign="top" char=".">0.0064967</td>
<td align="char" valign="top" char=".">0.0073556</td>
<td align="center" valign="top">0.0090 (0, 0.0180) (<xref ref-type="bibr" rid="ref35">35</xref>)</td>
<td align="center" valign="top">RIMKCD (2018)</td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular mortality</td>
<td align="char" valign="top" char=".">0.0021664</td>
<td align="char" valign="top" char=".">0.0022355</td>
<td align="char" valign="top" char=".">0.0022003</td>
<td align="center" valign="top">0.0053 (0.0085, 0.0201) (<xref ref-type="bibr" rid="ref34">34</xref>)</td>
<td align="center" valign="top">RIMKCD (2018)</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory mortality</td>
<td align="char" valign="top" char=".">0.0006453</td>
<td align="char" valign="top" char=".">0.0005697</td>
<td align="char" valign="top" char=".">0.0006078</td>
<td align="center" valign="top">0.0143 (0.0085, 0.0201) (<xref ref-type="bibr" rid="ref34">34</xref>)</td>
<td align="center" valign="top">DHSR (2016)</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer mortality</td>
<td align="char" valign="top" char=".">0.0008456</td>
<td align="char" valign="top" char=".">0.0004147</td>
<td align="char" valign="top" char=".">0.0006345</td>
<td align="center" valign="top">0.0340 (0, 0.0710) (<xref ref-type="bibr" rid="ref34">34</xref>)</td>
<td align="center" valign="top">RIMKCD (2018)</td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="char" valign="top" char=".">0.0154545</td>
<td align="char" valign="top" char=".">0.0185454</td>
<td align="char" valign="top" char=".">0.017</td>
<td align="center" valign="top">0.0068 (0.0043, 0.0093) (<xref ref-type="bibr" rid="ref18">18</xref>)</td>
<td align="center" valign="top">CHSY (2021)</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="char" valign="top" char=".">0.0199091</td>
<td align="char" valign="top" char=".">0.0238909</td>
<td align="char" valign="top" char=".">0.0219</td>
<td align="center" valign="top">0.0109 (0, 0.0221) (<xref ref-type="bibr" rid="ref18">18</xref>)</td>
<td align="center" valign="top">CHSY (2021)</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="char" valign="top" char=".">0.0009505</td>
<td align="char" valign="top" char=".">0.0005583</td>
<td align="char" valign="top" char=".">0.0007554</td>
<td align="center" valign="top">0.0340 (0, 0.0710) (<xref ref-type="bibr" rid="ref27">27</xref>)</td>
<td align="center" valign="top">RIMKCD (2018)</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In a large population, the occurrence of disease can be regarded as a low probability event (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref27">27</xref>). Therefore, its probability of occurrence should conform to the Poisson distribution (<xref ref-type="bibr" rid="ref34">34</xref>). In this study, the health risk was calculated with the PM exposure response function which was the WHO recommended model for health effect estimation in high PM concentration area (WHO, 2006). There are four major factors in the <xref ref-type="disp-formula" rid="EQ1">Equations (1)</xref> and <xref ref-type="disp-formula" rid="EQ2">(2)</xref>, which including population size, PM<sub>2.5</sub> concentration, exposure response coefficient, and the baseline incidence of health endpoint.</p>
<disp-formula id="EQ1"><label>(1)</label><mml:math id="M1"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mi>exp</mml:mi><mml:mfenced close="]" open="["><mml:mrow><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mfenced><mml:mrow><mml:mi>C</mml:mi><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced></mml:mrow></mml:math></disp-formula>
<disp-formula id="EQ2"><label>(2)</label><mml:math id="M2"><mml:mrow><mml:mi>&#x0394;</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mfenced><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mi>P</mml:mi><mml:mfenced close="}" open="{"><mml:mrow><mml:mn>1</mml:mn><mml:mo>&#x2212;</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mi>exp</mml:mi><mml:mfenced close="]" open="["><mml:mrow><mml:msub><mml:mi>&#x03B2;</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mfenced><mml:mrow><mml:mi>C</mml:mi><mml:mo>&#x2212;</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced><mml:msub><mml:mi>E</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></disp-formula>
<p>Here, <italic>E</italic><sub>0</sub> is baseline incidence of a disease, <italic>E</italic><sub>i</sub> is the estimated incidence of health endpoint <italic>i</italic> under PM<sub>2.5</sub> concentration <italic>C</italic>, <italic>C</italic><sub>0</sub> is the baseline concentration of PM<sub>2.5</sub> (set as 5&#x2009;&#x03BC;g/m<sup>3</sup>, the new WHO annual guideline concentration), <italic>C</italic> is the exposure concentration of PM<sub>2.5</sub>, <italic>&#x03B2;<sub>i</sub></italic> is the exposure response coefficient. <italic>P</italic> is the population size, <italic>&#x2206;E</italic> is for population health risks associated with PM<sub>2.5</sub> pollution. In this study, <italic>C</italic><sub>0</sub> refers to the new WHO standards.</p>
</sec>
<sec id="sec7">
<title>PM<sub>2.5</sub> economic effect assessment</title>
<p>In this study, health economic losses were estimated using health risk assessment results and average disease costs. The economic effect of PM<sub>2.5</sub> was assessed with the following equation:</p>
<disp-formula id="EQ3"><label>(3)</label><mml:math id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">EC</mml:mi></mml:mrow><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>&#x0394;</mml:mi><mml:mi>E</mml:mi><mml:mo>&#x22C5;</mml:mo><mml:mi>cos</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></disp-formula>
<p>where EC<italic><sub>i</sub></italic> is the total cost of health endpoint <italic>i</italic>; Cost<italic><sub>i</sub></italic> is the cost per case.</p>
<p>Here, the health economic effect of hospitalization was estimated using the cost of illness (COI) method (<xref ref-type="bibr" rid="ref7">7</xref>, <xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref27">27</xref>). Hospitalization costs were obtained from the SBHDSP 2021. Premature death cost was estimated using the method of value of statistical life (VSL) (<xref ref-type="bibr" rid="ref37">37</xref>). VSL refers to the willingness-to-pay of patients to avoid risk of death. Since willingness-to-pay usually increases with people&#x2019;s income, a adjusted equation was utilized to correct VSL in this study (<xref ref-type="bibr" rid="ref27">27</xref>). The <italic>per capita</italic> income was obtained from Shandong Statistical Yearbook. The adjusted equation of VSL as following:</p>
<disp-formula id="EQ4"><label>(4)</label><mml:math id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">VSL</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">now</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant="normal">VSL</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">past</mml:mi></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Income</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">now</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">Income</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">past</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mi>e</mml:mi></mml:msup></mml:mrow></mml:math></disp-formula>
<p>where VSL<sub>now</sub> and VSL<sub>past</sub> refers to current and past willingness to pay; Income<sub>now</sub> and Income<sub>past</sub> represents current and past <italic>per capita</italic> income; <italic>e</italic> is an elastic coefficient of willingness-to-pay assumed to be 0.8. In this study, VSL was adjusted twice because it lacked a reliable reference value in Shandong. Firstly, it was adjusted to get VSL<sub>2021</sub> based on VSL<sub>2016</sub> in Jinan. Then, it was adjusted again to get VSL<sub>Shandong</sub> based on the VSL<sub>Jinan</sub> in 2021.</p>
</sec>
</sec>
<sec sec-type="results" id="sec8">
<title>Results and discussion</title>
<sec id="sec9">
<title>PM<sub>2.5</sub> concentration status report</title>
<p>Pollutant concentration is one of the important factors affecting health risk assessment results (<xref ref-type="bibr" rid="ref38 ref39 ref40">38&#x2013;40</xref>). High levels of PM<sub>2.5</sub> exposure will increase the risk of some health endpoints such as respiratory, cardiovascular and lung diseases (<xref ref-type="bibr" rid="ref41 ref42 ref43">41&#x2013;43</xref>). As shown in <xref rid="fig2" ref-type="fig">Figure 2</xref>, the PM<sub>2.5</sub> concentration was relatively low in the area of Shandong Peninsula. While it had a high concentration in the western area of Shandong province. Industrial distribution and regional differences, as well as unbalanced economic development, might lead to the spatial differences in PM<sub>2.5</sub> concentration in Shandong. The PM<sub>2.5</sub> concentrations of 16 cities in Shandong Province have been provided in <xref rid="tab1" ref-type="table">Table 1</xref>. Therefore, the health effects of PM<sub>2.5</sub> pollution were likely to be greater in the western area of Shandong province without considering the influence of population density factor. PM<sub>2.5</sub> pollution might have great impact on the four cities including Heze, Jining, Zibo, and Liaocheng.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>PM<sub>2.5</sub> concentrations in 16 cities of Shandong Province in 2021.</p>
</caption>
<graphic xlink:href="fpubh-11-1250572-g002.tif"/>
</fig>
</sec>
<sec id="sec10">
<title>PM<sub>2.5</sub> health risk assessment</title>
<p>As shown in <xref rid="tab2" ref-type="table">Table 2</xref>, the baseline incidences of health endpoints were provided. The incidences of cardiovascular and respiratory diseases were obtained from China Health Statistics Yearbook (CHSY, 2021)<xref rid="fn0025" ref-type="fn"><sup>7</sup></xref> Male/Female incidences were calculated based on the hospitalization rate of residents and the sex ratio of hospitalized patients in the national survey data in 2018. Due to the exposure-response coefficient of lung cancer morbidity cannot be obtained, its coefficient referred to lung-cancer mortality in this work.</p>
<p>The evaluation result showed that the premature death related to PM<sub>2.5</sub> pollution contributed 3.16% of all-cause deaths (shown in <xref rid="tab3" ref-type="table">Table 3</xref>). Among them, the proportion of male was 1.79%, and the proportion of female was 1.37%. Cardiovascular mortality, respiratory mortality, and lung-cancer mortality related to PM<sub>2.5</sub> pollution contributed 1.85, 5.08, and 12.51%, respectively, to annual cases of these health endpoints. Cardiovascular hospital admission, respiratory hospital admission, and lung-cancer morbidity related to PM<sub>2.5</sub> pollution contributed 2.38, 3.85, and 12.51% to yearly cases of these health endpoints. The four health endpoints related to PM<sub>2.5</sub> pollution including all-cause mortality, respiratory mortality, lung-cancer mortality, and lung-cancer morbidity in male were higher than those in female. The contribution of cardiovascular mortality in male and female was roughly equal. The contributions of cardiovascular and respiratory hospital admission in female were higher than those in male. This result was consistent with the findings reported by Bell et al. (<xref ref-type="bibr" rid="ref44">44</xref>) and Sang et al. (<xref ref-type="bibr" rid="ref45">45</xref>). Bell et al. (<xref ref-type="bibr" rid="ref44">44</xref>) pointed out that women might be more susceptible to PM<sub>2.5</sub>-related hospitalizations for some respiratory and cardiovascular causes. Sang et al. (<xref ref-type="bibr" rid="ref45">45</xref>) suggested that global ambient PM<sub>2.5</sub> pollution caused more premature deaths and consumption in men than in women. Therefore, PM<sub>2.5</sub> pollution had a greater impact on respiratory mortality and lung-cancer mortality and morbidity in male. And it also made a significant contribution to all-cause premature deaths in male. While it played an import role on cardiovascular and respiratory hospital admission in female. As a whole, the health consequences of PM<sub>2.5</sub> pollution appeared to be more severe in male than in female. For male, more attention should be paid to daily physical examination to reduce the premature death risk from diseases related to PM<sub>2.5</sub> pollution, especially respiratory system examination including lungs and respiratory tract.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Health effect of PM<sub>2.5</sub> in Shandong in 2021.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Gender</th>
<th align="left" valign="top">Health endpoints</th>
<th align="center" valign="top">WeiHai</th>
<th align="center" valign="top">YanTai</th>
<th align="center" valign="top">QingDao</th>
<th align="center" valign="top">Rizhao</th>
<th align="center" valign="top">DongYing</th>
<th align="center" valign="top">WeiFang</th>
<th align="center" valign="top">JiNan</th>
<th align="center" valign="top">BinZhou</th>
<th align="center" valign="top">TaiAn</th>
<th align="center" valign="top">DeZhou</th>
<th align="center" valign="top">LinYi</th>
<th align="center" valign="top">ZaoZhuang</th>
<th align="center" valign="top">LiaoCheng</th>
<th align="center" valign="top">ZiBo</th>
<th align="center" valign="top">JiNing</th>
<th align="center" valign="top">HeZe</th>
<th align="center" valign="top">Sum.</th>
<th align="center" valign="top">Annual %&#x002A;&#x002A;</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="7">Male</td>
<td align="left" valign="top">All-cause mortality</td>
<td align="center" valign="top">203</td>
<td align="center" valign="top">577</td>
<td align="center" valign="top">850</td>
<td align="center" valign="top">292</td>
<td align="center" valign="top">252</td>
<td align="center" valign="top">1,166</td>
<td align="center" valign="top">1,194</td>
<td align="center" valign="top">519</td>
<td align="center" valign="top">765</td>
<td align="center" valign="top">788</td>
<td align="center" valign="top">1,622</td>
<td align="center" valign="top">605</td>
<td align="center" valign="top">941</td>
<td align="center" valign="top">736</td>
<td align="center" valign="top">1,358</td>
<td align="center" valign="top">1,483</td>
<td align="center" valign="top">13,349</td>
<td align="center" valign="top">1.79</td>
</tr>
<tr>
<td align="left" valign="top"><italic>Cardiovascular mortality</italic></td>
<td align="center" valign="top"><italic>32</italic></td>
<td align="center" valign="top"><italic>90</italic></td>
<td align="center" valign="top"><italic>132</italic></td>
<td align="center" valign="top"><italic>45</italic></td>
<td align="center" valign="top"><italic>39</italic></td>
<td align="center" valign="top"><italic>181</italic></td>
<td align="center" valign="top"><italic>185</italic></td>
<td align="center" valign="top"><italic>80</italic></td>
<td align="center" valign="top"><italic>119</italic></td>
<td align="center" valign="top"><italic>122</italic></td>
<td align="center" valign="top"><italic>251</italic></td>
<td align="center" valign="top"><italic>94</italic></td>
<td align="center" valign="top"><italic>146</italic></td>
<td align="center" valign="top"><italic>114</italic></td>
<td align="center" valign="top"><italic>210</italic></td>
<td align="center" valign="top"><italic>229</italic></td>
<td align="center" valign="top"><italic>2,068</italic></td>
<td align="center" valign="top"><italic>0.93</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Respiratory mortality</italic></td>
<td align="center" valign="top"><italic>26</italic></td>
<td align="center" valign="top"><italic>73</italic></td>
<td align="center" valign="top"><italic>107</italic></td>
<td align="center" valign="top"><italic>37</italic></td>
<td align="center" valign="top"><italic>32</italic></td>
<td align="center" valign="top"><italic>147</italic></td>
<td align="center" valign="top"><italic>151</italic></td>
<td align="center" valign="top"><italic>66</italic></td>
<td align="center" valign="top"><italic>97</italic></td>
<td align="center" valign="top"><italic>100</italic></td>
<td align="center" valign="top"><italic>205</italic></td>
<td align="center" valign="top"><italic>77</italic></td>
<td align="center" valign="top"><italic>119</italic></td>
<td align="center" valign="top"><italic>93</italic></td>
<td align="center" valign="top"><italic>172</italic></td>
<td align="center" valign="top"><italic>188</italic></td>
<td align="center" valign="top"><italic>1,689</italic></td>
<td align="center" valign="top"><italic>2.74</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Lung-cancer mortality</italic></td>
<td align="center" valign="top"><italic>81</italic></td>
<td align="center" valign="top"><italic>231</italic></td>
<td align="center" valign="top"><italic>342</italic></td>
<td align="center" valign="top"><italic>118</italic></td>
<td align="center" valign="top"><italic>102</italic></td>
<td align="center" valign="top"><italic>475</italic></td>
<td align="center" valign="top"><italic>487</italic></td>
<td align="center" valign="top"><italic>212</italic></td>
<td align="center" valign="top"><italic>313</italic></td>
<td align="center" valign="top"><italic>322</italic></td>
<td align="center" valign="top"><italic>665</italic></td>
<td align="center" valign="top"><italic>249</italic></td>
<td align="center" valign="top"><italic>387</italic></td>
<td align="center" valign="top"><italic>303</italic></td>
<td align="center" valign="top"><italic>559</italic></td>
<td align="center" valign="top"><italic>611</italic></td>
<td align="center" valign="top"><italic>5,458</italic></td>
<td align="center" valign="top"><italic>8.47</italic></td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="center" valign="top">289</td>
<td align="center" valign="top">821</td>
<td align="center" valign="top">1,210</td>
<td align="center" valign="top">416</td>
<td align="center" valign="top">359</td>
<td align="center" valign="top">1,658</td>
<td align="center" valign="top">1,697</td>
<td align="center" valign="top">738</td>
<td align="center" valign="top">1,088</td>
<td align="center" valign="top">1,120</td>
<td align="center" valign="top">2,305</td>
<td align="center" valign="top">860</td>
<td align="center" valign="top">1,337</td>
<td align="center" valign="top">1,045</td>
<td align="center" valign="top">1,929</td>
<td align="center" valign="top">2,106</td>
<td align="center" valign="top">18,978</td>
<td align="center" valign="top">1.10</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="center" valign="top">599</td>
<td align="center" valign="top">1,703</td>
<td align="center" valign="top">2,511</td>
<td align="center" valign="top">863</td>
<td align="center" valign="top">745</td>
<td align="center" valign="top">3,448</td>
<td align="center" valign="top">3,530</td>
<td align="center" valign="top">1,534</td>
<td align="center" valign="top">2,264</td>
<td align="center" valign="top">2,330</td>
<td align="center" valign="top">4,797</td>
<td align="center" valign="top">1,790</td>
<td align="center" valign="top">2,784</td>
<td align="center" valign="top">2,177</td>
<td align="center" valign="top">4,018</td>
<td align="center" valign="top">4,388</td>
<td align="center" valign="top">39,483</td>
<td align="center" valign="top">1.78</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="center" valign="top">91</td>
<td align="center" valign="top">260</td>
<td align="center" valign="top">384</td>
<td align="center" valign="top">133</td>
<td align="center" valign="top">115</td>
<td align="center" valign="top">534</td>
<td align="center" valign="top">548</td>
<td align="center" valign="top">238</td>
<td align="center" valign="top">352</td>
<td align="center" valign="top">362</td>
<td align="center" valign="top">747</td>
<td align="center" valign="top">279</td>
<td align="center" valign="top">435</td>
<td align="center" valign="top">341</td>
<td align="center" valign="top">629</td>
<td align="center" valign="top">687</td>
<td align="center" valign="top">6,135</td>
<td align="center" valign="top">8.00</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="7">Female</td>
<td align="left" valign="top">All-cause mortality</td>
<td align="center" valign="top">165</td>
<td align="center" valign="top">465</td>
<td align="center" valign="top">694</td>
<td align="center" valign="top">224</td>
<td align="center" valign="top">203</td>
<td align="center" valign="top">912</td>
<td align="center" valign="top">965</td>
<td align="center" valign="top">405</td>
<td align="center" valign="top">596</td>
<td align="center" valign="top">609</td>
<td align="center" valign="top">1,203</td>
<td align="center" valign="top">438</td>
<td align="center" valign="top">706</td>
<td align="center" valign="top">593</td>
<td align="center" valign="top">1,013</td>
<td align="center" valign="top">1,077</td>
<td align="center" valign="top">10,268</td>
<td align="center" valign="top">1.37</td>
</tr>
<tr>
<td align="left" valign="top"><italic>Cardiovascular mortality</italic></td>
<td align="center" valign="top"><italic>33</italic></td>
<td align="center" valign="top"><italic>94</italic></td>
<td align="center" valign="top"><italic>140</italic></td>
<td align="center" valign="top"><italic>45</italic></td>
<td align="center" valign="top"><italic>41</italic></td>
<td align="center" valign="top"><italic>184</italic></td>
<td align="center" valign="top"><italic>194</italic></td>
<td align="center" valign="top"><italic>81</italic></td>
<td align="center" valign="top"><italic>120</italic></td>
<td align="center" valign="top"><italic>122</italic></td>
<td align="center" valign="top"><italic>242</italic></td>
<td align="center" valign="top"><italic>88</italic></td>
<td align="center" valign="top"><italic>142</italic></td>
<td align="center" valign="top"><italic>119</italic></td>
<td align="center" valign="top"><italic>204</italic></td>
<td align="center" valign="top"><italic>217</italic></td>
<td align="center" valign="top"><italic>2,067</italic></td>
<td align="center" valign="top"><italic>0.93</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Respiratory mortality</italic></td>
<td align="center" valign="top"><italic>23</italic></td>
<td align="center" valign="top"><italic>65</italic></td>
<td align="center" valign="top"><italic>97</italic></td>
<td align="center" valign="top"><italic>31</italic></td>
<td align="center" valign="top"><italic>29</italic></td>
<td align="center" valign="top"><italic>128</italic></td>
<td align="center" valign="top"><italic>136</italic></td>
<td align="center" valign="top"><italic>57</italic></td>
<td align="center" valign="top"><italic>84</italic></td>
<td align="center" valign="top"><italic>86</italic></td>
<td align="center" valign="top"><italic>169</italic></td>
<td align="center" valign="top"><italic>62</italic></td>
<td align="center" valign="top"><italic>99</italic></td>
<td align="center" valign="top"><italic>84</italic></td>
<td align="center" valign="top"><italic>143</italic></td>
<td align="center" valign="top"><italic>152</italic></td>
<td align="center" valign="top"><italic>1,444</italic></td>
<td align="center" valign="top"><italic>2.34</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Lung-cancer mortality</italic></td>
<td align="center" valign="top"><italic>41</italic></td>
<td align="center" valign="top"><italic>115</italic></td>
<td align="center" valign="top"><italic>172</italic></td>
<td align="center" valign="top"><italic>56</italic></td>
<td align="center" valign="top"><italic>51</italic></td>
<td align="center" valign="top"><italic>229</italic></td>
<td align="center" valign="top"><italic>243</italic></td>
<td align="center" valign="top"><italic>102</italic></td>
<td align="center" valign="top"><italic>151</italic></td>
<td align="center" valign="top"><italic>154</italic></td>
<td align="center" valign="top"><italic>304</italic></td>
<td align="center" valign="top"><italic>111</italic></td>
<td align="center" valign="top"><italic>179</italic></td>
<td align="center" valign="top"><italic>151</italic></td>
<td align="center" valign="top"><italic>258</italic></td>
<td align="center" valign="top"><italic>274</italic></td>
<td align="center" valign="top"><italic>2,592</italic></td>
<td align="center" valign="top"><italic>4.02</italic></td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="center" valign="top">354</td>
<td align="center" valign="top">1,000</td>
<td align="center" valign="top">1,492</td>
<td align="center" valign="top">483</td>
<td align="center" valign="top">436</td>
<td align="center" valign="top">1,961</td>
<td align="center" valign="top">2,074</td>
<td align="center" valign="top">870</td>
<td align="center" valign="top">1,280</td>
<td align="center" valign="top">1,307</td>
<td align="center" valign="top">2,583</td>
<td align="center" valign="top">940</td>
<td align="center" valign="top">1,516</td>
<td align="center" valign="top">1,273</td>
<td align="center" valign="top">2,176</td>
<td align="center" valign="top">2,313</td>
<td align="center" valign="top">22,058</td>
<td align="center" valign="top">1.28</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="center" valign="top">735</td>
<td align="center" valign="top">2,075</td>
<td align="center" valign="top">3,096</td>
<td align="center" valign="top">1,002</td>
<td align="center" valign="top">907</td>
<td align="center" valign="top">4,076</td>
<td align="center" valign="top">4,313</td>
<td align="center" valign="top">1,809</td>
<td align="center" valign="top">2,663</td>
<td align="center" valign="top">2,720</td>
<td align="center" valign="top">5,375</td>
<td align="center" valign="top">1,957</td>
<td align="center" valign="top">3,158</td>
<td align="center" valign="top">2,652</td>
<td align="center" valign="top">4,532</td>
<td align="center" valign="top">4,818</td>
<td align="center" valign="top">45,889</td>
<td align="center" valign="top">2.06</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="center" valign="top">55</td>
<td align="center" valign="top">155</td>
<td align="center" valign="top">232</td>
<td align="center" valign="top">75</td>
<td align="center" valign="top">69</td>
<td align="center" valign="top">309</td>
<td align="center" valign="top">328</td>
<td align="center" valign="top">137</td>
<td align="center" valign="top">203</td>
<td align="center" valign="top">207</td>
<td align="center" valign="top">410</td>
<td align="center" valign="top">149</td>
<td align="center" valign="top">242</td>
<td align="center" valign="top">203</td>
<td align="center" valign="top">347</td>
<td align="center" valign="top">369</td>
<td align="center" valign="top">3,489</td>
<td align="center" valign="top">4.55</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="8">All</td>
<td align="left" valign="top">All-cause mortality</td>
<td align="center" valign="top">369</td>
<td align="center" valign="top">1,045</td>
<td align="center" valign="top">1,550</td>
<td align="center" valign="top">517</td>
<td align="center" valign="top">457</td>
<td align="center" valign="top">2,081</td>
<td align="center" valign="top">2,166</td>
<td align="center" valign="top">925</td>
<td align="center" valign="top">1,363</td>
<td align="center" valign="top">1,398</td>
<td align="center" valign="top">2,820</td>
<td align="center" valign="top">1,040</td>
<td align="center" valign="top">1,646</td>
<td align="center" valign="top">1,333</td>
<td align="center" valign="top">2,368</td>
<td align="center" valign="top">2,553</td>
<td align="center" valign="top">23,628</td>
<td align="center" valign="top">3.16</td>
</tr>
<tr>
<td align="left" valign="top"><italic>Cardiovascular mortality</italic></td>
<td align="center" valign="top"><italic>65</italic></td>
<td align="center" valign="top"><italic>183</italic></td>
<td align="center" valign="top"><italic>272</italic></td>
<td align="center" valign="top"><italic>91</italic></td>
<td align="center" valign="top"><italic>80</italic></td>
<td align="center" valign="top"><italic>364</italic></td>
<td align="center" valign="top"><italic>379</italic></td>
<td align="center" valign="top"><italic>162</italic></td>
<td align="center" valign="top"><italic>238</italic></td>
<td align="center" valign="top"><italic>244</italic></td>
<td align="center" valign="top"><italic>493</italic></td>
<td align="center" valign="top"><italic>182</italic></td>
<td align="center" valign="top"><italic>288</italic></td>
<td align="center" valign="top"><italic>233</italic></td>
<td align="center" valign="top"><italic>414</italic></td>
<td align="center" valign="top"><italic>446</italic></td>
<td align="center" valign="top"><italic>4,134</italic></td>
<td align="center" valign="top"><italic>1.85</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Respiratory mortality</italic></td>
<td align="center" valign="top"><italic>49</italic></td>
<td align="center" valign="top"><italic>138</italic></td>
<td align="center" valign="top"><italic>205</italic></td>
<td align="center" valign="top"><italic>68</italic></td>
<td align="center" valign="top"><italic>60</italic></td>
<td align="center" valign="top"><italic>276</italic></td>
<td align="center" valign="top"><italic>287</italic></td>
<td align="center" valign="top"><italic>123</italic></td>
<td align="center" valign="top"><italic>181</italic></td>
<td align="center" valign="top"><italic>185</italic></td>
<td align="center" valign="top"><italic>374</italic></td>
<td align="center" valign="top"><italic>138</italic></td>
<td align="center" valign="top"><italic>218</italic></td>
<td align="center" valign="top"><italic>177</italic></td>
<td align="center" valign="top"><italic>314</italic></td>
<td align="center" valign="top"><italic>339</italic></td>
<td align="center" valign="top"><italic>3,132</italic></td>
<td align="center" valign="top"><italic>5.08</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Lung-cancer mortality</italic></td>
<td align="center" valign="top"><italic>123</italic></td>
<td align="center" valign="top"><italic>350</italic></td>
<td align="center" valign="top"><italic>520</italic></td>
<td align="center" valign="top"><italic>174</italic></td>
<td align="center" valign="top"><italic>155</italic></td>
<td align="center" valign="top"><italic>707</italic></td>
<td align="center" valign="top"><italic>738</italic></td>
<td align="center" valign="top"><italic>315</italic></td>
<td align="center" valign="top"><italic>465</italic></td>
<td align="center" valign="top"><italic>477</italic></td>
<td align="center" valign="top"><italic>964</italic></td>
<td align="center" valign="top"><italic>356</italic></td>
<td align="center" valign="top"><italic>565</italic></td>
<td align="center" valign="top"><italic>458</italic></td>
<td align="center" valign="top"><italic>814</italic></td>
<td align="center" valign="top"><italic>879</italic></td>
<td align="center" valign="top"><italic>8,061</italic></td>
<td align="center" valign="top"><italic>12.51</italic></td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="center" valign="top">643</td>
<td align="center" valign="top">1,820</td>
<td align="center" valign="top">2,699</td>
<td align="center" valign="top">900</td>
<td align="center" valign="top">795</td>
<td align="center" valign="top">3,621</td>
<td align="center" valign="top">3,768</td>
<td align="center" valign="top">1,609</td>
<td align="center" valign="top">2,370</td>
<td align="center" valign="top">2,430</td>
<td align="center" valign="top">4,903</td>
<td align="center" valign="top">1,807</td>
<td align="center" valign="top">2,861</td>
<td align="center" valign="top">2,317</td>
<td align="center" valign="top">4,116</td>
<td align="center" valign="top">4,437</td>
<td align="center" valign="top">41,096</td>
<td align="center" valign="top">2.38</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="center" valign="top">1,332</td>
<td align="center" valign="top">3,775</td>
<td align="center" valign="top">5,600</td>
<td align="center" valign="top">1,868</td>
<td align="center" valign="top">1,651</td>
<td align="center" valign="top">7,529</td>
<td align="center" valign="top">7,837</td>
<td align="center" valign="top">3,346</td>
<td align="center" valign="top">4,932</td>
<td align="center" valign="top">5,057</td>
<td align="center" valign="top">10,204</td>
<td align="center" valign="top">3,763</td>
<td align="center" valign="top">5,957</td>
<td align="center" valign="top">4,826</td>
<td align="center" valign="top">8,574</td>
<td align="center" valign="top">9,244</td>
<td align="center" valign="top">85,496</td>
<td align="center" valign="top">3.85</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="center" valign="top">147</td>
<td align="center" valign="top">417</td>
<td align="center" valign="top">619</td>
<td align="center" valign="top">207</td>
<td align="center" valign="top">184</td>
<td align="center" valign="top">842</td>
<td align="center" valign="top">878</td>
<td align="center" valign="top">375</td>
<td align="center" valign="top">554</td>
<td align="center" valign="top">568</td>
<td align="center" valign="top">1,148</td>
<td align="center" valign="top">424</td>
<td align="center" valign="top">673</td>
<td align="center" valign="top">545</td>
<td align="center" valign="top">969</td>
<td align="center" valign="top">1,046</td>
<td align="center" valign="top">9,597</td>
<td align="center" valign="top">12.51</td>
</tr>
<tr>
<td align="left" valign="top">Sum.&#x002A;</td>
<td align="center" valign="top">2,490</td>
<td align="center" valign="top">7,056</td>
<td align="center" valign="top">10,467</td>
<td align="center" valign="top">3,493</td>
<td align="center" valign="top">3,087</td>
<td align="center" valign="top">14,074</td>
<td align="center" valign="top">14,649</td>
<td align="center" valign="top">6,255</td>
<td align="center" valign="top">9,219</td>
<td align="center" valign="top">9,453</td>
<td align="center" valign="top">19,075</td>
<td align="center" valign="top">7,035</td>
<td align="center" valign="top">11,136</td>
<td align="center" valign="top">9,021</td>
<td align="center" valign="top">16,028</td>
<td align="center" valign="top">17,279</td>
<td align="center" valign="top">159,817</td>
<td align="center" valign="top">/</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Male&#x2009;+&#x2009;Female&#x2260;All. Baseline incidence was calculated separately, which caused this error (&#x003C;3%).</p>
<p>&#x002A;Sum&#x2009;=&#x2009;<italic>All cause mortality&#x2009;+&#x2009;Respiratory hospital admission&#x2009;+&#x2009;Cardiovascular hospital admission&#x2009;+&#x2009;Lung-cancer morbidity</italic>.</p>
<p>&#x002A;&#x002A;Cases caused by PM<sub>2.5</sub>/annual cases of this health endpoint.</p>
<p>Italics: All-cause mortality including Cardiovascular mortality, Respiratory mortality, and Lung-cancer mortality.</p>
</table-wrap-foot>
</table-wrap>
<p>Based on the evaluation results of this study, the number of premature deaths and illnesses related to PM<sub>2.5</sub> pollution in Shandong Province reached 159,817 in 2021. Without considering population density, LinYi, HeZe, JiNing, JiNan and WeiFang had higher health risks caused by PM<sub>2.5</sub> pollution. In each of these cities, more than 14,000 people experienced premature death or morbidity due to PM<sub>2.5</sub> pollution. Only three cities, WeiHai, DongYing and Rizhao, were less impacted by PM<sub>2.5</sub> pollution in terms of health risk. The number of premature deaths and illnesses affected by PM<sub>2.5</sub> in each of these areas was less than 5,000. Therefore, further strengthening the control of PM<sub>2.5</sub> emission will have a positive effect on population health, especially in areas with high health risks.</p>
</sec>
<sec id="sec11">
<title>PM<sub>2.5</sub> health economic costs</title>
<p>Health economic effect assessment is an important means to evaluate the economic burden of environmental pollution to a city (<xref ref-type="bibr" rid="ref46 ref47 ref48 ref49">46&#x2013;49</xref>). The value of statistical life (VSL) method was a common method to assess the health cost of premature death in previous studies (<xref ref-type="bibr" rid="ref34">34</xref>). The occurrence of respiratory and cardiovascular diseases is closely related to PM pollution, which has been confirmed in many previous studies (<xref ref-type="bibr" rid="ref50 ref51 ref52 ref53 ref54">50&#x2013;54</xref>). Therefore, the hospitalization costs for respiratory and cardiovascular diseases were also assessed in addition to premature death endpoint in this study. Since the exact cost of each disease could not be obtained, the mean hospitalization cost was selected as the reference value for calculation in this work. Owing to the high mortality rate of lung-cancer, its health cost was estimated using VSL method in this study. The costs of premature death and hospitalization were shown in <xref rid="tab4" ref-type="table">Table 4</xref>. Finally, the economic effect related to PM<sub>2.5</sub> pollution was assessed based on the result of health risk assessment.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Health cost situation.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Health endpoints</th>
<th align="center" valign="top" colspan="4">Costs (US$)</th>
<th align="center" valign="top" rowspan="2">Approach</th>
<th align="center" valign="top" rowspan="2">References</th>
</tr>
<tr>
<th align="center" valign="top">Hospital</th>
<th align="center" valign="top">Community health center</th>
<th align="center" valign="top">Town and township hospital</th>
<th align="center" valign="top">Mean</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Mortality</td>
<td align="center" valign="top">/</td>
<td align="center" valign="top">/</td>
<td align="center" valign="top">/</td>
<td align="center" valign="top">219,000</td>
<td align="center" valign="top">VSL</td>
<td align="center" valign="top">Yin et al. (<xref ref-type="bibr" rid="ref35">35</xref>)</td>
</tr>
<tr>
<td align="left" valign="top">Hospital admission</td>
<td align="center" valign="top">1,753</td>
<td align="center" valign="top">607</td>
<td align="center" valign="top">450</td>
<td align="center" valign="top">937</td>
<td align="center" valign="top">COI</td>
<td align="center" valign="top">SBHDSP (2021)</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Some scholars suggested that the health economic costs caused by PM<sub>2.5</sub> pollution could be around 1% of GDP (<xref ref-type="bibr" rid="ref7">7</xref>, <xref ref-type="bibr" rid="ref22">22</xref>). As shown in <xref rid="tab5" ref-type="table">Table 5</xref>, the health economic loss of each heath endpoint related to PM<sub>2.5</sub> pollution was estimated. It resulted in nearly 7.4 billions dollars in healthy economic cost, which accounted for 0.57% of GDP in Shandong in 2021. This result was basically consistent with our previous study in Beijing. It accounted for 0.87, 0.54, and 0.45% of GDP in Beijing during 2014&#x2013;2016, respectively (<xref ref-type="bibr" rid="ref34">34</xref>). The percentage of health economic loss in GDP was lower than other long-term exposure studies in China (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref27">27</xref>, <xref ref-type="bibr" rid="ref35">35</xref>). It may be due to the failure to account for outpatient costs, such as asthma, acute bronchitis and chronic bronchitis. In addition, the reduction of air pollution in China may also be a factor in the falling economic costs of health (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref55">55</xref>, <xref ref-type="bibr" rid="ref56">56</xref>).</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Health economic effect of PM<sub>2.5</sub> exposure in Shandong in 2021 (million US$).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Gender</th>
<th align="left" valign="top">Health endpoints</th>
<th align="center" valign="top">WeiHai</th>
<th align="center" valign="top">YanTai</th>
<th align="center" valign="top">QingDao</th>
<th align="center" valign="top">Rizhao</th>
<th align="center" valign="top">DongYing</th>
<th align="center" valign="top">WeiFang</th>
<th align="center" valign="top">JiNan</th>
<th align="center" valign="top">BinZhou</th>
<th align="center" valign="top">TaiAn</th>
<th align="center" valign="top">DeZhou</th>
<th align="center" valign="top">LinYi</th>
<th align="center" valign="top">ZaoZhuang</th>
<th align="center" valign="top">LiaoCheng</th>
<th align="center" valign="top">ZiBo</th>
<th align="center" valign="top">JiNing</th>
<th align="center" valign="top">HeZe</th>
<th align="center" valign="top">Sum.</th>
<th align="center" valign="top">EC<sub>i</sub>/GDP (%)&#x002A;&#x002A;</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="7">Male</td>
<td align="left" valign="top">All-cause mortality</td>
<td align="char" valign="top" char=".">44.5</td>
<td align="char" valign="top" char=".">126.4</td>
<td align="char" valign="top" char=".">186.2</td>
<td align="char" valign="top" char=".">63.9</td>
<td align="char" valign="top" char=".">55.2</td>
<td align="char" valign="top" char=".">255.4</td>
<td align="char" valign="top" char=".">261.5</td>
<td align="char" valign="top" char=".">113.7</td>
<td align="char" valign="top" char=".">167.5</td>
<td align="char" valign="top" char=".">172.6</td>
<td align="char" valign="top" char=".">355.2</td>
<td align="char" valign="top" char=".">132.5</td>
<td align="char" valign="top" char=".">206.1</td>
<td align="char" valign="top" char=".">161.2</td>
<td align="char" valign="top" char=".">297.4</td>
<td align="char" valign="top" char=".">324.8</td>
<td align="char" valign="top" char=".">2923.9</td>
<td align="char" valign="top" char=".">0.227</td>
</tr>
<tr>
<td align="left" valign="top"><italic>Cardiovascular mortality</italic></td>
<td align="char" valign="top" char="."><italic>7.0</italic></td>
<td align="char" valign="top" char="."><italic>19.7</italic></td>
<td align="char" valign="top" char="."><italic>28.9</italic></td>
<td align="char" valign="top" char="."><italic>9.9</italic></td>
<td align="char" valign="top" char="."><italic>8.5</italic></td>
<td align="char" valign="top" char="."><italic>39.6</italic></td>
<td align="char" valign="top" char="."><italic>40.5</italic></td>
<td align="char" valign="top" char="."><italic>17.5</italic></td>
<td align="char" valign="top" char="."><italic>26.1</italic></td>
<td align="char" valign="top" char="."><italic>26.7</italic></td>
<td align="char" valign="top" char="."><italic>55.0</italic></td>
<td align="char" valign="top" char="."><italic>20.6</italic></td>
<td align="char" valign="top" char="."><italic>32.0</italic></td>
<td align="char" valign="top" char="."><italic>25.0</italic></td>
<td align="char" valign="top" char="."><italic>46.0</italic></td>
<td align="char" valign="top" char="."><italic>50.2</italic></td>
<td align="char" valign="top" char="."><italic>453.1</italic></td>
<td align="char" valign="top" char="."><italic>0.035</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Respiratory mortality</italic></td>
<td align="char" valign="top" char="."><italic>5.7</italic></td>
<td align="char" valign="top" char="."><italic>16.0</italic></td>
<td align="char" valign="top" char="."><italic>23.4</italic></td>
<td align="char" valign="top" char="."><italic>8.1</italic></td>
<td align="char" valign="top" char="."><italic>7.0</italic></td>
<td align="char" valign="top" char="."><italic>32.2</italic></td>
<td align="char" valign="top" char="."><italic>33.1</italic></td>
<td align="char" valign="top" char="."><italic>14.5</italic></td>
<td align="char" valign="top" char="."><italic>21.2</italic></td>
<td align="char" valign="top" char="."><italic>21.9</italic></td>
<td align="char" valign="top" char="."><italic>44.9</italic></td>
<td align="char" valign="top" char="."><italic>16.9</italic></td>
<td align="char" valign="top" char="."><italic>26.1</italic></td>
<td align="char" valign="top" char="."><italic>20.4</italic></td>
<td align="char" valign="top" char="."><italic>37.7</italic></td>
<td align="char" valign="top" char="."><italic>41.2</italic></td>
<td align="char" valign="top" char="."><italic>370.1</italic></td>
<td align="char" valign="top" char="."><italic>0.029</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Lung-cancer mortality</italic></td>
<td align="char" valign="top" char="."><italic>17.7</italic></td>
<td align="char" valign="top" char="."><italic>50.6</italic></td>
<td align="char" valign="top" char="."><italic>74.9</italic></td>
<td align="char" valign="top" char="."><italic>25.8</italic></td>
<td align="char" valign="top" char="."><italic>22.3</italic></td>
<td align="char" valign="top" char="."><italic>104.0</italic></td>
<td align="char" valign="top" char="."><italic>106.7</italic></td>
<td align="char" valign="top" char="."><italic>46.4</italic></td>
<td align="char" valign="top" char="."><italic>68.5</italic></td>
<td align="char" valign="top" char="."><italic>70.5</italic></td>
<td align="char" valign="top" char="."><italic>145.6</italic></td>
<td align="char" valign="top" char="."><italic>54.5</italic></td>
<td align="char" valign="top" char="."><italic>84.8</italic></td>
<td align="char" valign="top" char="."><italic>66.4</italic></td>
<td align="char" valign="top" char="."><italic>122.4</italic></td>
<td align="char" valign="top" char="."><italic>133.8</italic></td>
<td align="char" valign="top" char="."><italic>1195.1</italic></td>
<td align="char" valign="top" char="."><italic>0.093</italic></td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="char" valign="top" char=".">0.3</td>
<td align="char" valign="top" char=".">0.8</td>
<td align="char" valign="top" char=".">1.1</td>
<td align="char" valign="top" char=".">0.4</td>
<td align="char" valign="top" char=".">0.3</td>
<td align="char" valign="top" char=".">1.6</td>
<td align="char" valign="top" char=".">1.6</td>
<td align="char" valign="top" char=".">0.7</td>
<td align="char" valign="top" char=".">1.0</td>
<td align="char" valign="top" char=".">1.0</td>
<td align="char" valign="top" char=".">2.2</td>
<td align="char" valign="top" char=".">0.8</td>
<td align="char" valign="top" char=".">1.3</td>
<td align="char" valign="top" char=".">1.0</td>
<td align="char" valign="top" char=".">1.8</td>
<td align="char" valign="top" char=".">2.0</td>
<td align="char" valign="top" char=".">17.8</td>
<td align="char" valign="top" char=".">0.001</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="char" valign="top" char=".">0.6</td>
<td align="char" valign="top" char=".">1.6</td>
<td align="char" valign="top" char=".">2.4</td>
<td align="char" valign="top" char=".">0.8</td>
<td align="char" valign="top" char=".">0.7</td>
<td align="char" valign="top" char=".">3.2</td>
<td align="char" valign="top" char=".">3.3</td>
<td align="char" valign="top" char=".">1.4</td>
<td align="char" valign="top" char=".">2.1</td>
<td align="char" valign="top" char=".">2.2</td>
<td align="char" valign="top" char=".">4.5</td>
<td align="char" valign="top" char=".">1.7</td>
<td align="char" valign="top" char=".">2.6</td>
<td align="char" valign="top" char=".">2.0</td>
<td align="char" valign="top" char=".">3.8</td>
<td align="char" valign="top" char=".">4.1</td>
<td align="char" valign="top" char=".">37.0</td>
<td align="char" valign="top" char=".">0.003</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="char" valign="top" char=".">19.9</td>
<td align="char" valign="top" char=".">56.9</td>
<td align="char" valign="top" char=".">84.1</td>
<td align="char" valign="top" char=".">29.1</td>
<td align="char" valign="top" char=".">25.2</td>
<td align="char" valign="top" char=".">116.9</td>
<td align="char" valign="top" char=".">120.0</td>
<td align="char" valign="top" char=".">52.1</td>
<td align="char" valign="top" char=".">77.1</td>
<td align="char" valign="top" char=".">79.3</td>
<td align="char" valign="top" char=".">163.6</td>
<td align="char" valign="top" char=".">61.1</td>
<td align="char" valign="top" char=".">95.3</td>
<td align="char" valign="top" char=".">74.7</td>
<td align="char" valign="top" char=".">137.8</td>
<td align="char" valign="top" char=".">150.5</td>
<td align="char" valign="top" char=".">1343.6</td>
<td align="char" valign="top" char=".">0.104</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="7">Female</td>
<td align="left" valign="top">All-cause mortality</td>
<td align="char" valign="top" char=".">36.1</td>
<td align="char" valign="top" char=".">101.8</td>
<td align="char" valign="top" char=".">152.0</td>
<td align="char" valign="top" char=".">49.1</td>
<td align="char" valign="top" char=".">44.5</td>
<td align="char" valign="top" char=".">199.7</td>
<td align="char" valign="top" char=".">211.3</td>
<td align="char" valign="top" char=".">88.7</td>
<td align="char" valign="top" char=".">130.5</td>
<td align="char" valign="top" char=".">133.4</td>
<td align="char" valign="top" char=".">263.5</td>
<td align="char" valign="top" char=".">95.9</td>
<td align="char" valign="top" char=".">154.6</td>
<td align="char" valign="top" char=".">129.9</td>
<td align="char" valign="top" char=".">221.8</td>
<td align="char" valign="top" char=".">235.9</td>
<td align="char" valign="top" char=".">2248.7</td>
<td align="char" valign="top" char=".">0.175</td>
</tr>
<tr>
<td align="left" valign="top"><italic>Cardiovascular mortality</italic></td>
<td align="char" valign="top" char="."><italic>7.2</italic></td>
<td align="char" valign="top" char="."><italic>20.6</italic></td>
<td align="char" valign="top" char="."><italic>30.7</italic></td>
<td align="char" valign="top" char="."><italic>9.9</italic></td>
<td align="char" valign="top" char="."><italic>9.0</italic></td>
<td align="char" valign="top" char="."><italic>40.3</italic></td>
<td align="char" valign="top" char="."><italic>42.5</italic></td>
<td align="char" valign="top" char="."><italic>17.7</italic></td>
<td align="char" valign="top" char="."><italic>26.3</italic></td>
<td align="char" valign="top" char="."><italic>26.7</italic></td>
<td align="char" valign="top" char="."><italic>53.0</italic></td>
<td align="char" valign="top" char="."><italic>19.3</italic></td>
<td align="char" valign="top" char="."><italic>31.1</italic></td>
<td align="char" valign="top" char="."><italic>26.1</italic></td>
<td align="char" valign="top" char="."><italic>44.7</italic></td>
<td align="char" valign="top" char="."><italic>47.5</italic></td>
<td align="char" valign="top" char="."><italic>452.5</italic></td>
<td align="char" valign="top" char="."><italic>0.035</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Respiratory mortality</italic></td>
<td align="char" valign="top" char="."><italic>5.0</italic></td>
<td align="char" valign="top" char="."><italic>14.2</italic></td>
<td align="char" valign="top" char="."><italic>21.2</italic></td>
<td align="char" valign="top" char="."><italic>6.8</italic></td>
<td align="char" valign="top" char="."><italic>6.4</italic></td>
<td align="char" valign="top" char="."><italic>28.0</italic></td>
<td align="char" valign="top" char="."><italic>29.8</italic></td>
<td align="char" valign="top" char="."><italic>12.5</italic></td>
<td align="char" valign="top" char="."><italic>18.4</italic></td>
<td align="char" valign="top" char="."><italic>18.8</italic></td>
<td align="char" valign="top" char="."><italic>37.0</italic></td>
<td align="char" valign="top" char="."><italic>13.6</italic></td>
<td align="char" valign="top" char="."><italic>21.7</italic></td>
<td align="char" valign="top" char="."><italic>18.4</italic></td>
<td align="char" valign="top" char="."><italic>31.3</italic></td>
<td align="char" valign="top" char="."><italic>33.3</italic></td>
<td align="char" valign="top" char="."><italic>316.5</italic></td>
<td align="char" valign="top" char="."><italic>0.025</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Lung-cancer mortality</italic></td>
<td align="char" valign="top" char="."><italic>9.0</italic></td>
<td align="char" valign="top" char="."><italic>25.2</italic></td>
<td align="char" valign="top" char="."><italic>37.7</italic></td>
<td align="char" valign="top" char="."><italic>12.3</italic></td>
<td align="char" valign="top" char="."><italic>11.2</italic></td>
<td align="char" valign="top" char="."><italic>50.2</italic></td>
<td align="char" valign="top" char="."><italic>53.2</italic></td>
<td align="char" valign="top" char="."><italic>22.3</italic></td>
<td align="char" valign="top" char="."><italic>33.1</italic></td>
<td align="char" valign="top" char="."><italic>33.7</italic></td>
<td align="char" valign="top" char="."><italic>66.6</italic></td>
<td align="char" valign="top" char="."><italic>24.3</italic></td>
<td align="char" valign="top" char="."><italic>39.2</italic></td>
<td align="char" valign="top" char="."><italic>33.1</italic></td>
<td align="char" valign="top" char="."><italic>56.5</italic></td>
<td align="char" valign="top" char="."><italic>60.0</italic></td>
<td align="char" valign="top" char="."><italic>567.4</italic></td>
<td align="char" valign="top" char="."><italic>0.044</italic></td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="char" valign="top" char=".">0.3</td>
<td align="char" valign="top" char=".">0.9</td>
<td align="char" valign="top" char=".">1.4</td>
<td align="char" valign="top" char=".">0.5</td>
<td align="char" valign="top" char=".">0.4</td>
<td align="char" valign="top" char=".">1.8</td>
<td align="char" valign="top" char=".">1.9</td>
<td align="char" valign="top" char=".">0.8</td>
<td align="char" valign="top" char=".">1.2</td>
<td align="char" valign="top" char=".">1.2</td>
<td align="char" valign="top" char=".">2.4</td>
<td align="char" valign="top" char=".">0.9</td>
<td align="char" valign="top" char=".">1.4</td>
<td align="char" valign="top" char=".">1.2</td>
<td align="char" valign="top" char=".">2.0</td>
<td align="char" valign="top" char=".">2.2</td>
<td align="char" valign="top" char=".">20.7</td>
<td align="char" valign="top" char=".">0.002</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="char" valign="top" char=".">0.7</td>
<td align="char" valign="top" char=".">1.9</td>
<td align="char" valign="top" char=".">2.9</td>
<td align="char" valign="top" char=".">0.9</td>
<td align="char" valign="top" char=".">0.8</td>
<td align="char" valign="top" char=".">3.8</td>
<td align="char" valign="top" char=".">4.0</td>
<td align="char" valign="top" char=".">1.7</td>
<td align="char" valign="top" char=".">2.5</td>
<td align="char" valign="top" char=".">2.5</td>
<td align="char" valign="top" char=".">5.0</td>
<td align="char" valign="top" char=".">1.8</td>
<td align="char" valign="top" char=".">3.0</td>
<td align="char" valign="top" char=".">2.5</td>
<td align="char" valign="top" char=".">4.2</td>
<td align="char" valign="top" char=".">4.5</td>
<td align="char" valign="top" char=".">43.0</td>
<td align="char" valign="top" char=".">0.003</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="char" valign="top" char=".">12.0</td>
<td align="char" valign="top" char=".">33.9</td>
<td align="char" valign="top" char=".">50.8</td>
<td align="char" valign="top" char=".">16.4</td>
<td align="char" valign="top" char=".">15.1</td>
<td align="char" valign="top" char=".">67.7</td>
<td align="char" valign="top" char=".">71.8</td>
<td align="char" valign="top" char=".">30.0</td>
<td align="char" valign="top" char=".">44.5</td>
<td align="char" valign="top" char=".">45.3</td>
<td align="char" valign="top" char=".">89.8</td>
<td align="char" valign="top" char=".">32.6</td>
<td align="char" valign="top" char=".">53.0</td>
<td align="char" valign="top" char=".">44.5</td>
<td align="char" valign="top" char=".">76.0</td>
<td align="char" valign="top" char=".">80.8</td>
<td align="char" valign="top" char=".">764.3</td>
<td align="char" valign="top" char=".">0.059</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="9">All</td>
<td align="left" valign="top">All-cause mortality</td>
<td align="char" valign="top" char=".">80.8</td>
<td align="char" valign="top" char=".">228.9</td>
<td align="char" valign="top" char=".">339.5</td>
<td align="char" valign="top" char=".">113.2</td>
<td align="char" valign="top" char=".">100.1</td>
<td align="char" valign="top" char=".">455.7</td>
<td align="char" valign="top" char=".">474.4</td>
<td align="char" valign="top" char=".">202.6</td>
<td align="char" valign="top" char=".">298.5</td>
<td align="char" valign="top" char=".">306.2</td>
<td align="char" valign="top" char=".">617.6</td>
<td align="char" valign="top" char=".">227.8</td>
<td align="char" valign="top" char=".">360.5</td>
<td align="char" valign="top" char=".">291.9</td>
<td align="char" valign="top" char=".">518.6</td>
<td align="char" valign="top" char=".">559.1</td>
<td align="char" valign="top" char=".">5175.2</td>
<td align="char" valign="top" char=".">0.402</td>
</tr>
<tr>
<td align="left" valign="top"><italic>Cardiovascular mortality</italic></td>
<td align="char" valign="top" char="."><italic>14.2</italic></td>
<td align="char" valign="top" char="."><italic>40.1</italic></td>
<td align="char" valign="top" char="."><italic>59.6</italic></td>
<td align="char" valign="top" char="."><italic>19.9</italic></td>
<td align="char" valign="top" char="."><italic>17.5</italic></td>
<td align="char" valign="top" char="."><italic>79.7</italic></td>
<td align="char" valign="top" char="."><italic>83.0</italic></td>
<td align="char" valign="top" char="."><italic>35.5</italic></td>
<td align="char" valign="top" char="."><italic>52.1</italic></td>
<td align="char" valign="top" char="."><italic>53.4</italic></td>
<td align="char" valign="top" char="."><italic>108.0</italic></td>
<td align="char" valign="top" char="."><italic>39.9</italic></td>
<td align="char" valign="top" char="."><italic>63.1</italic></td>
<td align="char" valign="top" char="."><italic>51.0</italic></td>
<td align="char" valign="top" char="."><italic>90.7</italic></td>
<td align="char" valign="top" char="."><italic>97.7</italic></td>
<td align="char" valign="top" char="."><italic>905.3</italic></td>
<td align="char" valign="top" char="."><italic>0.070</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Respiratory mortality</italic></td>
<td align="char" valign="top" char="."><italic>10.7</italic></td>
<td align="char" valign="top" char="."><italic>30.2</italic></td>
<td align="char" valign="top" char="."><italic>44.9</italic></td>
<td align="char" valign="top" char="."><italic>14.9</italic></td>
<td align="char" valign="top" char="."><italic>13.1</italic></td>
<td align="char" valign="top" char="."><italic>60.4</italic></td>
<td align="char" valign="top" char="."><italic>62.9</italic></td>
<td align="char" valign="top" char="."><italic>26.9</italic></td>
<td align="char" valign="top" char="."><italic>39.6</italic></td>
<td align="char" valign="top" char="."><italic>40.5</italic></td>
<td align="char" valign="top" char="."><italic>81.9</italic></td>
<td align="char" valign="top" char="."><italic>30.2</italic></td>
<td align="char" valign="top" char="."><italic>47.7</italic></td>
<td align="char" valign="top" char="."><italic>38.8</italic></td>
<td align="char" valign="top" char="."><italic>68.8</italic></td>
<td align="char" valign="top" char="."><italic>74.2</italic></td>
<td align="char" valign="top" char="."><italic>685.9</italic></td>
<td align="char" valign="top" char="."><italic>0.053</italic></td>
</tr>
<tr>
<td align="left" valign="top"><italic>Lung-cancer mortality</italic></td>
<td align="char" valign="top" char="."><italic>26.9</italic></td>
<td align="char" valign="top" char="."><italic>76.7</italic></td>
<td align="char" valign="top" char="."><italic>113.9</italic></td>
<td align="char" valign="top" char="."><italic>38.1</italic></td>
<td align="char" valign="top" char="."><italic>33.9</italic></td>
<td align="char" valign="top" char="."><italic>154.8</italic></td>
<td align="char" valign="top" char="."><italic>161.6</italic></td>
<td align="char" valign="top" char="."><italic>69.0</italic></td>
<td align="char" valign="top" char="."><italic>101.8</italic></td>
<td align="char" valign="top" char="."><italic>104.5</italic></td>
<td align="char" valign="top" char="."><italic>211.1</italic></td>
<td align="char" valign="top" char="."><italic>78.0</italic></td>
<td align="char" valign="top" char="."><italic>123.7</italic></td>
<td align="char" valign="top" char="."><italic>100.3</italic></td>
<td align="char" valign="top" char="."><italic>178.3</italic></td>
<td align="char" valign="top" char="."><italic>192.5</italic></td>
<td align="char" valign="top" char="."><italic>1765.1</italic></td>
<td align="char" valign="top" char="."><italic>0.137</italic></td>
</tr>
<tr>
<td align="left" valign="top">Cardiovascular hospital admission</td>
<td align="char" valign="top" char=".">0.6</td>
<td align="char" valign="top" char=".">1.7</td>
<td align="char" valign="top" char=".">2.5</td>
<td align="char" valign="top" char=".">0.8</td>
<td align="char" valign="top" char=".">0.7</td>
<td align="char" valign="top" char=".">3.4</td>
<td align="char" valign="top" char=".">3.5</td>
<td align="char" valign="top" char=".">1.5</td>
<td align="char" valign="top" char=".">2.2</td>
<td align="char" valign="top" char=".">2.3</td>
<td align="char" valign="top" char=".">4.6</td>
<td align="char" valign="top" char=".">1.7</td>
<td align="char" valign="top" char=".">2.7</td>
<td align="char" valign="top" char=".">2.2</td>
<td align="char" valign="top" char=".">3.9</td>
<td align="char" valign="top" char=".">4.2</td>
<td align="char" valign="top" char=".">38.5</td>
<td align="char" valign="top" char=".">0.003</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory hospital admission</td>
<td align="char" valign="top" char=".">1.2</td>
<td align="char" valign="top" char=".">3.5</td>
<td align="char" valign="top" char=".">5.2</td>
<td align="char" valign="top" char=".">1.8</td>
<td align="char" valign="top" char=".">1.5</td>
<td align="char" valign="top" char=".">7.1</td>
<td align="char" valign="top" char=".">7.3</td>
<td align="char" valign="top" char=".">3.1</td>
<td align="char" valign="top" char=".">4.6</td>
<td align="char" valign="top" char=".">4.7</td>
<td align="char" valign="top" char=".">9.6</td>
<td align="char" valign="top" char=".">3.5</td>
<td align="char" valign="top" char=".">5.6</td>
<td align="char" valign="top" char=".">4.5</td>
<td align="char" valign="top" char=".">8.0</td>
<td align="char" valign="top" char=".">8.7</td>
<td align="char" valign="top" char=".">80.1</td>
<td align="char" valign="top" char=".">0.006</td>
</tr>
<tr>
<td align="left" valign="top">Lung-cancer morbidity</td>
<td align="char" valign="top" char=".">32.2</td>
<td align="char" valign="top" char=".">91.3</td>
<td align="char" valign="top" char=".">135.6</td>
<td align="char" valign="top" char=".">45.3</td>
<td align="char" valign="top" char=".">40.3</td>
<td align="char" valign="top" char=".">184.4</td>
<td align="char" valign="top" char=".">192.3</td>
<td align="char" valign="top" char=".">82.1</td>
<td align="char" valign="top" char=".">121.3</td>
<td align="char" valign="top" char=".">124.4</td>
<td align="char" valign="top" char=".">251.4</td>
<td align="char" valign="top" char=".">92.9</td>
<td align="char" valign="top" char=".">147.4</td>
<td align="char" valign="top" char=".">119.4</td>
<td align="char" valign="top" char=".">212.2</td>
<td align="char" valign="top" char=".">229.1</td>
<td align="char" valign="top" char=".">2101.5</td>
<td align="char" valign="top" char=".">0.163</td>
</tr>
<tr>
<td align="left" valign="top">Sum.&#x002A;</td>
<td align="char" valign="top" char=".">114.9</td>
<td align="char" valign="top" char=".">325.4</td>
<td align="char" valign="top" char=".">482.8</td>
<td align="char" valign="top" char=".">161.1</td>
<td align="char" valign="top" char=".">142.7</td>
<td align="char" valign="top" char=".">650.6</td>
<td align="char" valign="top" char=".">677.5</td>
<td align="char" valign="top" char=".">289.3</td>
<td align="char" valign="top" char=".">426.7</td>
<td align="char" valign="top" char=".">437.6</td>
<td align="char" valign="top" char=".">883.1</td>
<td align="char" valign="top" char=".">325.8</td>
<td align="char" valign="top" char=".">516.1</td>
<td align="char" valign="top" char=".">418.0</td>
<td align="char" valign="top" char=".">742.7</td>
<td align="char" valign="top" char=".">801.0</td>
<td align="char" valign="top" char=".">7395.3</td>
<td align="char" valign="top" char=".">0.574</td>
</tr>
<tr>
<td align="left" valign="top">EC<sub>city</sub>/GDP<sub>city</sub>(%)#</td>
<td align="char" valign="top" char=".">0.21</td>
<td align="char" valign="top" char=".">0.24</td>
<td align="char" valign="top" char=".">0.22</td>
<td align="char" valign="top" char=".">0.47</td>
<td align="char" valign="top" char=".">0.27</td>
<td align="char" valign="top" char=".">0.60</td>
<td align="char" valign="top" char=".">0.38</td>
<td align="char" valign="top" char=".">0.65</td>
<td align="char" valign="top" char=".">0.92</td>
<td align="char" valign="top" char=".">0.81</td>
<td align="char" valign="top" char=".">1.04</td>
<td align="char" valign="top" char=".">1.08</td>
<td align="char" valign="top" char=".">1.26</td>
<td align="char" valign="top" char=".">0.64</td>
<td align="char" valign="top" char=".">0.95</td>
<td align="char" valign="top" char=".">1.30</td>
<td align="char" valign="top" char=".">0.57</td>
<td align="char" valign="top" char=".">/</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;Sum&#x2009;=&#x2009;All cause mortality&#x2009;+&#x2009;Respiratory hospital admission&#x2009;+&#x2009;Cardiovascular hospital admission&#x2009;+&#x2009;Lung-cancer morbidity.</p>
<p>&#x002A;&#x002A;Economic costs caused by health endpoint <italic>i</italic>/the GDP in Shandong in 2021.</p>
<p><sup>#</sup> Economic costs caused by PM2.5/the local GDP in 2021.</p>
<p>Italics: All-cause mortality including Cardiovascular mortality, Respiratory mortality, and Lung-cancer mortality.</p>
</table-wrap-foot>
</table-wrap>
<p>Overall, the health economic effects were higher for male than for female in Shandong. The health economic costs of male and female accounted for 0.336 and 0.239% of GDP, respectively. In terms of the health effects in cities, LinYi, HeZe, JiNing, JiNan, WeiFang, and LiaoCheng were the cities where the health economic cost was more than 500 millions. For the proportion of health economic cost, HeZe, LiaoCheng, ZaoZhuang, and LinYi were the cities where it was more than 1% of GDP, accounted for 1.30, 1.26, 1.08, and 1.04% of the GDP in local areas. On the whole, the economic cost of health in highly polluted and densely populated areas in Shandong was higher than that in other cities. It also led to a heavier fiscal burden for these areas.</p>
</sec>
<sec id="sec12">
<title>Policies implication</title>
<p>Since the publication of the WHO Air Quality Guidelines - Global Update 2005 (AQG2005), it has had a positive impact on air pollution control policies around the world (<xref ref-type="bibr" rid="ref57">57</xref>, <xref ref-type="bibr" rid="ref58">58</xref>). AQG2005 provided the first globally referenced framework for air pollution control targets and established transitional targets based on the potential risk of death from long-term exposure to each pollutant (<xref ref-type="bibr" rid="ref59 ref60 ref61">59&#x2013;61</xref>). It was then adopted by many highly polluted regions and countries as progressive targets for the gradual reduction of air pollution (<xref ref-type="bibr" rid="ref62">62</xref>). China also updated its Air Quality Standards in 2012, and included PM<sub>2.5</sub> and O<sub>3</sub> in monitoring projects for the first time (<xref ref-type="bibr" rid="ref63">63</xref>). With the progress of science, the monitoring capabilities of environmental and health and the level of exposure and risk assessment had gradually improved (<xref ref-type="bibr" rid="ref64">64</xref>). It led a significant increase in scientific evidence of the health hazards of air pollution (<xref ref-type="bibr" rid="ref65">65</xref>, <xref ref-type="bibr" rid="ref66">66</xref>). Finally, WHO updated the AQG again in September 2021 on the basis of comprehensive analysis and scientific assessment of the literature and results over the past 15&#x2009;years. Air quality standards have become more stringent.</p>
<p>As air quality standards have been ever more stringent, PM<sub>2.5</sub> health guideline has also been changed and further reduced. Population health risks and economic effects assessed based on the new WHO standards should be higher than that using the previous air quality standards. However, the increase of health risks and economic costs related to PM<sub>2.5</sub> pollution was not very significant compared with our previous study. Considering these differences in population, economy, and environment, making a direct comparison between Shandong and Beijing may not be entirely appropriate. The total health effects and economic losses caused by PM<sub>2.5</sub> pollution may vary greatly in the two regions. Therefore, in order to reduce the uncertain impact of these factors, this study only compared the proportion of PM<sub>2.5</sub> pollution-related health endpoints and the proportion of economic loss in local GDP between the two regions. Finally, whether it was the proportion of affected population or the proportion of health economic costs, the results of this study were comparable to our previous assessment of Beijing in 2015 (<xref ref-type="bibr" rid="ref34">34</xref>). In our previous study of Beijing, it was the Class II limit values of the National Ambient Air Quality Standard (35&#x2009;&#x03BC;g/m<sup>3</sup>) that used as the baseline concentration to complete the health risk assessment work. It was a full 30&#x2009;&#x03BC;g/m<sup>3</sup> higher than the baseline concentration used in this study. The fact that the health and economic effects related to PM<sub>2.5</sub> did not increase significantly under the stricter standards can only be attributed to the possibility that China&#x2019;s air pollution control measures were having a positive effect. The annual PM<sub>2.5</sub> concentration assessed in this study should be at least 30&#x2009;&#x03BC;g/m<sup>3</sup> lower than that in Beijing in 2015. In fact, the PM<sub>2.5</sub> concentration in Beijing was 80.6&#x2009;&#x03BC;g/m<sup>3</sup> in 2015, while it was 39&#x2009;&#x03BC;g/m<sup>3</sup> in Shandong in 2021. Therefore, with the positive effect of China&#x2019;s air pollution control measures, the nationwide decrease in PM<sub>2.5</sub> concentration was the main reason why the health and economic effects related to PM<sub>2.5</sub> pollution had not increased substantially in this study. China&#x2019;s air quality improvement strategy had started to pay off, which was confirmed in this study from the perspective of health risk assessment.</p>
<p>Although the results of this study were mainly based on the analysis of PM<sub>2.5</sub> pollution in Shandong Province, they still provided side evidence for the positive effects of air quality improvement strategies in China. In the follow-up studies, strengthening regional difference analysis and long-term assessment may be more valuable for evaluating China&#x2019;s air quality prevention and control strategies. In addition, how to tailor the prevention and control strategies of different regions according to the health risks of regional populations should also attract the attention of decision-making departments. Reducing population health risks should be the ultimate goal of improving air quality.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec13">
<title>Conclusion</title>
<p>In this study, the exposure response function was used to assess the health risks of PM<sub>2.5</sub> pollution in Shandong Province. The cost of illness (COI) method and value of statistical life (VSL) method were used to estimate the health economic losses associated with PM<sub>2.5</sub> pollution. The new WHO (2021) Health Guidelines were used as the PM<sub>2.5</sub> baseline concentration in this study. The health risks and economic effects of PM<sub>2.5</sub> exposure in 16 cities in Shandong Province were assessed separately. Results showed that despite a 30&#x2009;&#x03BC;g/m<sup>3</sup> reduction in PM<sub>2.5</sub> baseline concentration compared to our previous study, there was no significant increase in health risks and economic losses. About 159.8 thousand people died or became ill prematurely due to PM<sub>2.5</sub> pollution, which caused a health economic loss of about 7.4 billion dollars in Shandong. The health economic cost accounted for about 0.57% of GDP in Shandong in 2021. It was similar to our previous assessment of the economic effects of PM<sub>2.5</sub> pollution in Beijing in 2015. Therefore, under the more stringent criteria, there was no qualitative change in the assessment of health risks and economic losses, which proved that China&#x2019;s air pollution prevention and control strategy might already be having a positive effect.</p>
</sec>
<sec sec-type="data-availability" id="sec14">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="sec15">
<title>Author contributions</title>
<p>XX: Writing &#x2013; review and editing, Conceptualization, Methodology, Project administration. WZ: Writing &#x2013; review and editing. XS: Review and editing. ZS: Writing &#x2013; review and editing, Formal Analysis. WC: Review and editing. YW: Writing &#x2013; review and editing. HM: Writing &#x2013; review and editing. TL: Writing &#x2013; review and editing. ZW: Review and editing. All authors contributed to the article and approved the submitted version.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec16">
<title>Funding</title>
<p>This work was funded by Opening Project of Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3) (no. FDLAP21001); Shandong Provincial Natural Science Foundation (no. ZR2023QB037); Key R&#x0026;D Program of Shandong Province, China (No. ZR2022QB144); Pilot project of integration of science, education and production of Qilu University of Technology (Shandong Academy of Sciences) (nos. 2022PX038, 2022GH020); Project of Shandong Society for Environmental (no. 202213); the National Research Program for key issues in air pollution control (no. DQGG202123).</p>
</sec>
<sec sec-type="COI-statement" id="sec17">
<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 id="sec100" sec-type="disclaimer">
<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>
<fn-group>
<fn id="fn0001"><p><sup>1</sup><ext-link xlink:href="http://xxgk.sdein.gov.cn/xxgkml/hjzkgb/202206/P020220607364593369389.pdf" ext-link-type="uri">http://xxgk.sdein.gov.cn/xxgkml/hjzkgb/202206/P020220607364593369389.pdf</ext-link></p></fn>
<fn id="fn0002"><p><sup>2</sup><ext-link xlink:href="http://www.shandong.gov.cn/art/2022/1/24/art_305267_10331968.html?xxgkhide=1" ext-link-type="uri">http://www.shandong.gov.cn/art/2022/1/24/art_305267_10331968.html?xxgkhide=1</ext-link></p></fn>
<fn id="fn0003"><p><sup>3</sup><ext-link xlink:href="http://sthj.shandong.gov.cn/zwgk/sqcspm/" ext-link-type="uri">http://sthj.shandong.gov.cn/zwgk/sqcspm/</ext-link></p></fn>
<fn id="fn0004"><p><sup>4</sup><ext-link xlink:href="http://www.shandong.gov.cn/art/2023/1/10/art_305258_10333917.html)," ext-link-type="uri">http://www.shandong.gov.cn/art/2023/1/10/art_305258_10333917.html</ext-link></p></fn>
<fn id="fn0005"><p><sup>5</sup><ext-link xlink:href="http://tjj.shandong.gov.cn/art/2021/5/21/art_156112_10287516.html" ext-link-type="uri">http://tjj.shandong.gov.cn/art/2021/5/21/art_156112_10287516.html</ext-link></p></fn>
<fn id="fn0006"><p><sup>6</sup><ext-link xlink:href="http://tjj.shandong.gov.cn/tjnj/nj2022/zk/zk/indexch.htm" ext-link-type="uri">http://tjj.shandong.gov.cn/tjnj/nj2022/zk/zk/indexch.htm</ext-link></p></fn>
<fn id="fn0025"><p><sup>7</sup><ext-link xlink:href="https://www.doc88.com/p-11461558491027.html" ext-link-type="uri">https://www.doc88.com/p-11461558491027.html</ext-link></p></fn>
</fn-group>
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